Information Management System and Method

ABSTRACT

A computer-implemented method, computer program product and computing system for: monitoring a device to receive data signals indicative of the device; and processing the data signals over a defined period of time to automatically define one or more defined signal norms for the data signals.

RELATED APPLICATION(S)

This application claims the benefit of the following U.S. ProvisionalApplication Nos. 63/492,117 filed on 24 Mar. 2023; 63/492,137 filed on24 Mar. 2023; 63/492,145 filed on 24 Mar. 2023; and 63/359,129 filed on7 Jul. 2022; the entire contents of which are incorporated herein byreference.

TECHNICAL FIELD

This disclosure relates to information systems and methods and, moreparticularly, to information systems and methods that enable a pluralityof devices to communicate and/or be managed.

BACKGROUND

The lack of communication between medical devices can lead tosignificant problems in managing alarms on those devices. Alarms play acritical role in patient care, alerting healthcare providers to changesin a patient's condition or potential issues with medical devices.However, when devices are not able to communicate effectively with eachother, several challenges arise in managing alarms:

-   -   Lack of Context and Situational Awareness: Without communication        between devices, alarms may lack important context and        situational information. For example, a patient's vital signs        monitored by one device may trigger an alarm, but this alarm may        not be synchronized with alarms from other devices, such as        infusion pumps or ventilators. This lack of context can make it        challenging for healthcare providers to assess the urgency and        priority of each alarm.    -   Alarm Fatigue and Desensitization: Healthcare providers are        frequently exposed to a large number of alarms from various        devices. When alarms are not coordinated or synchronized, it can        result in an overwhelming number of alarms, leading to alarm        fatigue. Alarm fatigue occurs when healthcare providers become        desensitized to alarms due to their frequency, leading to        delayed or missed responses to critical alarms.    -   Inefficient Alarm Prioritization and Response: When alarms from        different devices are not communicated or integrated, it becomes        difficult to prioritize and respond to alarms effectively.        Without a centralized system for managing alarms, healthcare        providers may need to manually assess and prioritize each alarm        separately, potentially leading to delays in responding to        critical situations.    -   Increased Risk of Missed or Delayed Alarms: When devices do not        communicate, there is an increased risk of missed or delayed        alarms. For example, if a patient's oxygen saturation level is        dropping, an alarm from a pulse oximeter may not trigger an        alarm on other devices, such as a bedside monitor or nurse call        system, potentially delaying the necessary intervention.

The consequences of these problems can be severe, including compromisedpatient safety, adverse events, and suboptimal clinical outcomes.Moreover, the lack of communication between medical devices addscomplexity to healthcare provider workflows and can lead to increasedstress and burden on the clinical staff.

SUMMARY OF DISCLOSURE

Establishing Norms for a Device:

In one implementation, a computer-implemented method is executed on acomputing device and includes: monitoring a device to receive datasignals indicative of the device; and processing the data signals over adefined period of time to automatically define one or more definedsignal norms for the data signals.

One or more of the following features may be included. The data signalsmay concern one or more details of the device and/or uses of the device.The device may include one or more of: a medical device, a processcontrol device, a networking device, a computing device, a manufacturingdevice, an agricultural device, an energy/refining device, an aerospacedevice, a forestry device, and a defense device. Processing the datasignals over a defined period of time to automatically define one ormore defined signal norms for the data signals may include: examining arange of the data signals. Processing the data signals over a definedperiod of time to automatically define one or more defined signal normsfor the data signals may include: calculating one or more standarddeviations of the data signals. Processing the data signals over adefined period of time to automatically define one or more definedsignal norms for the data signals may include: iteratively redefiningthe one or more defined signal norms based upon updated data signalsreceived from the device. Processing the data signals over a definedperiod of time to automatically define one or more defined signal normsfor the data signals may include: continuously redefining the one ormore defined signal norms based upon updated data signals received fromthe device. The device may be monitored to receive subsequent datasignals indicative of the device. The subsequent data signals may becompared to the defined signal norms to identify outliers. The outliersmay be investigated to determine if an issue exists with the device.Outlier definition criteria may be adjusted to eliminate the outlier ifan issue does not exist. Investigating the outliers to determine if anissue exists with the device may include one or more of: physicallyinvestigating the outliers; and examining other data signals from thedevice. Adjusting the outlier definition criteria may include: definingbespoke outlier definition criteria for the device. The device mayinclude one or more sub devices.

In another implementation, a computer program product resides on acomputer readable medium and has a plurality of instructions stored onit. When executed by a processor, the instructions cause the processorto perform operations including: monitoring a device to receive datasignals indicative of the device; and processing the data signals over adefined period of time to automatically define one or more definedsignal norms for the data signals.

One or more of the following features may be included. The data signalsmay concern one or more details of the device and/or uses of the device.The device may include one or more of: a medical device, a processcontrol device, a networking device, a computing device, a manufacturingdevice, an agricultural device, an energy/refining device, an aerospacedevice, a forestry device, and a defense device. Processing the datasignals over a defined period of time to automatically define one ormore defined signal norms for the data signals may include: examining arange of the data signals. Processing the data signals over a definedperiod of time to automatically define one or more defined signal normsfor the data signals may include: calculating one or more standarddeviations of the data signals. Processing the data signals over adefined period of time to automatically define one or more definedsignal norms for the data signals may include: iteratively redefiningthe one or more defined signal norms based upon updated data signalsreceived from the device. Processing the data signals over a definedperiod of time to automatically define one or more defined signal normsfor the data signals may include: continuously redefining the one ormore defined signal norms based upon updated data signals received fromthe device. The device may be monitored to receive subsequent datasignals indicative of the device. The subsequent data signals may becompared to the defined signal norms to identify outliers. The outliersmay be investigated to determine if an issue exists with the device.Outlier definition criteria may be adjusted to eliminate the outlier ifan issue does not exist. Investigating the outliers to determine if anissue exists with the device may include one or more of: physicallyinvestigating the outliers; and examining other data signals from thedevice. Adjusting the outlier definition criteria may include: definingbespoke outlier definition criteria for the device. The device mayinclude one or more sub devices.

In another implementation, a computing system includes a processor and amemory system configured to perform operations including: monitoring adevice to receive data signals indicative of the device; and processingthe data signals over a defined period of time to automatically defineone or more defined signal norms for the data signals.

One or more of the following features may be included. The data signalsmay concern one or more details of the device and/or uses of the device.The device may include one or more of: a medical device, a processcontrol device, a networking device, a computing device, a manufacturingdevice, an agricultural device, an energy/refining device, an aerospacedevice, a forestry device, and a defense device. Processing the datasignals over a defined period of time to automatically define one ormore defined signal norms for the data signals may include: examining arange of the data signals. Processing the data signals over a definedperiod of time to automatically define one or more defined signal normsfor the data signals may include: calculating one or more standarddeviations of the data signals. Processing the data signals over adefined period of time to automatically define one or more definedsignal norms for the data signals may include: iteratively redefiningthe one or more defined signal norms based upon updated data signalsreceived from the device. Processing the data signals over a definedperiod of time to automatically define one or more defined signal normsfor the data signals may include: continuously redefining the one ormore defined signal norms based upon updated data signals received fromthe device. The device may be monitored to receive subsequent datasignals indicative of the device. The subsequent data signals may becompared to the defined signal norms to identify outliers. The outliersmay be investigated to determine if an issue exists with the device.Outlier definition criteria may be adjusted to eliminate the outlier ifan issue does not exist. Investigating the outliers to determine if anissue exists with the device may include one or more of: physicallyinvestigating the outliers; and examining other data signals from thedevice. Adjusting the outlier definition criteria may include: definingbespoke outlier definition criteria for the device. The device mayinclude one or more sub devices.

The details of one or more implementations are set forth in theaccompanying drawings and the description below. Other features andadvantages will become apparent from the description, the drawings, andthe claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic view of a distributed computing networkincluding a computing device that executes an information processaccording to an embodiment of the present disclosure;

FIG. 2 is a flowchart of the information process of FIG. 1 according toan embodiment of the present disclosure;

FIG. 3 is a diagrammatic view of multiple devices coupled to theinformation process of FIG. 1 according to an embodiment of the presentdisclosure;

FIG. 4 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 5 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 6 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 7 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 8 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 9 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 10 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 11 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 12 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 13A is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIGS. 13B-13D are diagrammatic views of a portion of an OperationsHealth UX rendered by the information process of FIG. 1 according to anembodiment of the present disclosure;

FIG. 14A is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIGS. 14B-14D are diagrammatic views of a portion of an IncidentPatterns UX rendered by the information process of FIG. 1 according toan embodiment of the present disclosure;

FIG. 15A is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIGS. 15B-15D are diagrammatic views of a portion of a Threshold ManagerUX rendered by the information process of FIG. 1 according to anembodiment of the present disclosure;

FIG. 16A is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure;

FIG. 16B is a diagrammatic view of a portion of an Alarm Insights UXrendered by the information process of FIG. 1 according to an embodimentof the present disclosure; and

FIG. 17 is another flowchart of the information process of FIG. 1according to an embodiment of the present disclosure.

Like reference symbols in the various drawings indicate like elements.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

System Overview

Referring to FIG. 1 , there is shown information process 10. Informationprocess 10 may be implemented as a server-side process, a client-sideprocess, or a hybrid server-side/client-side process. For example,information process 10 may be implemented as a purely server-sideprocess via information process 10 s. Alternatively, information process10 may be implemented as a purely client-side process via one or more ofinformation process 10 c 1, information process 10 c 2, informationprocess 10 c 3, and information process 10 c 4. Alternatively still,information process 10 may be implemented as a hybridserver-side/client-side process via information process 10 s incombination with one or more of information process 10 c 1, informationprocess 10 c 2, information process 10 c 3, and information process 10 c4. Accordingly, information process 10 as used in this disclosure mayinclude any combination of information process 10 s, information process10 c 1, information process 10 c 2, information process 10 c 3, andinformation process 10 c 4.

Information process 10 s may be a server application and may reside onand may be executed by computing device 12, which may be connected tonetwork 14 (e.g., the Internet or a local area network). Examples ofcomputing device 12 may include, but are not limited to: a personalcomputer, a server computer, a series of server computers, a minicomputer, a mainframe computer, or a cloud-based computing platform.

The instruction sets and subroutines of information process 10 s, whichmay be stored on storage device 16 coupled to computing device 12, maybe executed by one or more processors (not shown) and one or more memoryarchitectures (not shown) included within computing device 12. Examplesof storage device 16 may include but are not limited to: a hard diskdrive; a RAID device; a random-access memory (RAM); a read-only memory(ROM); and all forms of flash memory storage devices.

Network 14 may be connected to one or more secondary networks (e.g.,network 18), examples of which may include but are not limited to: alocal area network; a wide area network; or an intranet, for example.

Examples of information processes 10 c 1, 10 c 2, 10 c 3, 10 c 4 mayinclude but are not limited to a web browser, a game console userinterface, a mobile device user interface, or a specialized application(e.g., an application running on e.g., the Android™ platform, the iOS™platform, the Windows™ platform, the Linux™ platform or the UNIX™platform). The instruction sets and subroutines of information processes10 c 1, 10 c 3, 10 c 4, which may be stored on storage devices 20, 22,24, 26 (respectively) coupled to client electronic devices 28, 30, 32,34 (respectively), may be executed by one or more processors (not shown)and one or more memory architectures (not shown) incorporated intoclient electronic devices 28, 30, 32, 34 (respectively). Examples ofstorage devices 20, 22, 24, 26 may include but are not limited to: harddisk drives; RAID devices; random access memories (RAM); read-onlymemories (ROM), and all forms of flash memory storage devices.

Examples of client electronic devices 28, 30, 32, 34 may include, butare not limited to, a smartphone (not shown), a personal digitalassistant (not shown), a tablet computer (not shown), laptop computers28, 30, 32, personal computer 34, a notebook computer (not shown), aserver computer (not shown), a gaming console (not shown), and adedicated network device (not shown). Client electronic devices 28, 30,32, 34 may each execute an operating system, examples of which mayinclude but are not limited to Microsoft Windows™, Android™, iOS™,Linux™, or a custom operating system.

Users 36, 38, 40, 42 may access information process 10 directly throughnetwork 14 or through secondary network 18. Further, information process10 may be connected to network 14 through secondary network 18, asillustrated with link line 44.

The various client electronic devices (e.g., client electronic devices28, 30, 32, 34) may be directly or indirectly coupled to network 14 (ornetwork 18). For example, laptop computer 28 and laptop computer 30 areshown wirelessly coupled to network 14 via wireless communicationchannels 44, 46 (respectively) established between laptop computers 28,30 (respectively) and cellular network/bridge 48, which is showndirectly coupled to network 14. Further, laptop computer 32 is shownwirelessly coupled to network 14 via wireless communication channel 50established between laptop computer 32 and wireless access point (i.e.,WAP) 52, which is shown directly coupled to network 14. Additionally,personal computer 34 is shown directly coupled to network 18 via ahardwired network connection.

WAP 52 may be, for example, an IEEE 802.11a, 802.11b, 802.11g, 802.11n,Wi-Fi, and/or Bluetooth device that is capable of establishing wirelesscommunication channel 50 between laptop computer 32 and WAP 52. As isknown in the art, IEEE 802.11x specifications may use Ethernet protocoland carrier sense multiple access with collision avoidance (i.e.,CSMA/CA) for path sharing. As is known in the art, Bluetooth is atelecommunications industry specification that allows e.g., mobilephones, computers, and personal digital assistants to be interconnectedusing a short-range wireless connection.

Information Process Overview

As will be discussed below in greater detail, information process 10 maybe configured enable the analysis of working environments so that theworking conditions within these working environments may be ascertainedand defined . . . with specific attention being provided to minimizingworker attrition and maximizing worker wellbeing.

While many of the discussions below concern utilizing informationprocess 10 on medical devices within a medical environments, this is forillustrative purposes only and is not intended to be a limitation ofthis disclosure, as other configurations are possible and are consideredto be within the scope of this disclosure. For example, informationprocess 10 may be equally applicable to process control devices,networking devices, computing devices, manufacturing devices,agricultural devices, energy/refining devices, aerospace devices,forestry devices, and defense devices.

Cross-Vendor Middleware:

The following discussion concerns the manner in which informationprocess may be utilized to function as an intermediary between devicesthat are offered by multiple vendors. As is often the case, individualvendors tend to produce devices that that can communicate amongstthemselves but often have difficulties communicating with devicesprovided by other vendors. Accordingly and as will be discussed below,information process 10 may be configured to effectuate communicationbetween devices produced by different vendors.

Referring also to FIGS. 2-3 , information process 10 may receive 100data signals (e.g., data signals 200) from one or more first vendordevices (e.g., first vendor devices 202) and may receive 102 datasignals (e.g., data signals 204) from one or more second vendor devices(e.g., second vendor devices 206). Generally speaking, the data signals(e.g., data signals 200, 204) may concern one or more details of the oneor more first vendor devices (e.g., first vendor devices 202) and/or theone or more second vendor devices (e.g., second vendor devices 206).Additionally/alternatively, the data signals (e.g., data signals 200,204) may concern one or more uses of the one or more first vendordevices (e.g., first vendor devices 202) and/or the one or more secondvendor devices (e.g., second vendor devices 206).

The one or more first vendor devices (e.g., first vendor devices 202)may be coupled to information process 10 via e.g., wirelesscommunication channel 208 established between the one or more firstvendor devices (e.g., first vendor devices 202) and e.g., wirelessaccess point (i.e., WAP) 52. Additionally/alternatively, the one or morefirst vendor devices (e.g., first vendor devices 202) may be coupled toinformation process 10 via e.g., wired connection 210 establishedbetween the one or more first vendor devices (e.g., first vendor devices202) and e.g., network 14.

The one or more second vendor devices (e.g., second vendor devices 206)may be coupled to information process 10 via e.g., wirelesscommunication channel 212 established between the one or more secondvendor devices (e.g., second vendor devices 206) and e.g., wirelessaccess point (i.e., WAP) 52. Additionally/alternatively, the one or moresecond vendor devices (e.g., second vendor devices 206) may be coupledto information process 10 via e.g., wired connection 214 establishedbetween the one or more second vendor devices (e.g., second vendordevices 206) and e.g., network 14.

The one or more first vendor devices (e.g., first vendor devices 202)and/or the one or more second vendor devices (e.g., second vendordevices 206) may include one or more of: a medical device, a processcontrol device, a networking device, a computing device, a manufacturingdevice, an agricultural device, an energy/refining device, an aerospacedevice, a forestry device, and a defense device. Generally speaking,these vendor devices (e.g., first vendor devices 202 and/or secondvendor devices 206) may be electrically coupled to information process10 so that these vendor devices (e.g., first vendor devices 202 and/orsecond vendor devices 206) may provide the data signals (e.g., datasignals 200 and/or data signals 204) to information process 10.

Examples of MEDICAL DEVICES may include but are not limited toinstruments, apparatuses, machines, implants, or any other similar itemsused in the diagnosis, prevention, monitoring, treatment, or alleviationof diseases, injuries, or disabilities in humans. These devices arespecifically designed to serve medical purposes and are regulated byhealth authorities to ensure their safety and effectiveness.

Medical devices can range from simple tools such as thermometers andstethoscopes to more complex equipment like magnetic resonance imaging(MRI) machines, artificial organs, or robotic surgical systems. They areused by healthcare professionals, patients, or caregivers in varioushealthcare settings, including hospitals, clinics, laboratories, andeven at home.

Examples of medical devices may include but are not limited to:

-   -   Diagnostic Devices: These devices are used to identify diseases        or medical conditions. Examples include X-ray machines,        ultrasound scanners, blood pressure monitors, and glucose        meters.    -   Therapeutic Devices: These devices are used to treat or manage        medical conditions. Examples include pacemakers, insulin pumps,        dialysis machines, and prosthetic limbs.    -   Surgical Instruments: These devices are used during surgical        procedures to perform specific tasks. Examples include scalpels,        forceps, surgical lasers, and laparoscopic instruments.    -   Implants: These devices are surgically placed in the body to        support or replace a specific function. Examples include        artificial joints, dental implants, cardiac stents, and cochlear        implants.    -   Assistive Devices: These devices help individuals with        disabilities or limitations to improve their mobility or perform        daily activities. Examples include wheelchairs, hearing aids,        walkers, and canes.    -   Monitoring Devices: These devices are used to track and monitor        vital signs or specific health parameters. Examples include        electrocardiograms (ECGs), pulse oximeters, sleep apnea        monitors, and continuous glucose monitors.

It's important to note that the classification and regulation of medicaldevices may vary by country or region. Regulatory agencies, such as theU.S. Food and Drug Administration (FDA) in the United States, overseethe approval, safety, and quality of medical devices to ensure they meetthe necessary standards for patient care.

Examples of PROCESS CONTROL DEVICES (also known as industrial controldevices) may include but are not limited to instruments or equipmentused to monitor and regulate industrial processes to achieve desiredoutcomes such as efficiency, quality, safety, and consistency. Thesedevices are commonly employed in manufacturing, chemical processing,power generation, oil and gas refining, and other industrial sectors.They help automate and optimize processes, ensuring they operate withindefined parameters and maintain desired conditions.

Examples of process control devices may include but are not limited to:

-   -   Programmable Logic Controllers (PLCs): PLCs are versatile        digital computers that automate and control electromechanical        processes. They receive input signals from sensors, make        decisions based on pre-programmed logic, and send output signals        to actuators to control machinery or equipment.    -   Distributed Control Systems (DCS): DCSs are comprehensive        control systems used in large-scale industrial processes. They        consist of multiple control units interconnected with sensors,        actuators, and other devices. DCSs enable centralized monitoring        and control of various process variables across a plant or        facility.    -   Human-Machine Interface (HMI): HMIs provide a graphical        interface for operators to interact with process control        systems. They display real-time data, process status, alarms,        and allow operators to input commands or adjust parameters. HMIs        can be touchscreens, keypads, or other user-friendly interfaces.    -   Sensors and Transmitters: These devices are used to measure        physical or chemical variables such as temperature, pressure,        flow rate, level, pH, conductivity, and more. They convert these        measurements into electrical signals that can be interpreted and        used for control purposes.    -   Actuators: Actuators are devices responsible for converting        control signals into physical action. They control valves,        motors, pumps, and other equipment to adjust flow rates,        pressures, positions, or other process parameters based on        control system inputs.    -   Data Acquisition Systems: These systems collect and record data        from sensors, devices, and instruments at various points in the        process. They store this data for analysis, monitoring, and        historical reference to optimize process performance and        troubleshoot issues.    -   Control Valves: Control valves regulate fluid flow or pressure        in a process. They receive signals from the control system and        adjust their position or aperture to achieve the desired        setpoint.    -   Analytical Instruments: These instruments measure and analyze        chemical properties or composition in a process. Examples        include pH meters, gas analyzers, spectrometers, and        chromatographs.

Process control devices work together to enable real-time monitoring,analysis, and adjustment of industrial processes. They help improveefficiency, reduce errors, enhance safety, and ensure consistent productquality in a wide range of industries.

Examples of NETWORKING DEVICES may include but are not limited tohardware or software components that facilitate communication andconnectivity within a computer network. These devices enable thetransmission, routing, and management of data across networks, allowingdevices to communicate and share resources effectively. Networkingdevices play a crucial role in establishing and maintaining networkinfrastructure and connectivity.

Examples of networking devices may include but are not limited to:

-   -   Routers: Routers are essential devices that connect multiple        networks and facilitate the transfer of data between them. They        determine the optimal path for data packets to reach their        destination based on network addressing and routing protocols.    -   Switches: Switches are used to connect devices within a local        area network (LAN). They receive data packets and forward them        to the appropriate destination device based on the device's MAC        (Media Access Control) address. Switches help improve network        performance by enabling efficient data transfer between        connected devices.    -   Hubs: Hubs are simple network devices that operate at the        physical layer of a network. They receive incoming data packets        and broadcast them to all connected devices. However, unlike        switches, hubs do not have the capability to selectively forward        data to specific devices.    -   Modems: Modems are used to connect a network to an external        network or the Internet. They convert digital data from a        computer into analog signals suitable for transmission over        telephone lines (in the case of dial-up modems) or digital        signals for broadband connections.    -   Network Interface Cards (NICs): NICs are hardware components        installed in computers or devices to connect them to a network.        They provide the necessary interface for devices to transmit and        receive data over the network.    -   Wireless Access Points (WAPs): WAPs enable wireless connectivity        within a network. They serve as a central hub for wireless        devices to connect to a wired network, providing wireless access        and facilitating communication between wireless devices and the        network.    -   Firewalls: Firewalls are security devices that monitor and        control incoming and outgoing network traffic based on        predetermined security rules. They help protect networks from        unauthorized access, threats, and malicious activities.    -   Network Bridges: Bridges connect two or more LANs or network        segments and facilitate communication between them. They operate        at the data link layer of the network and can help extend        network coverage or segment networks to improve performance and        security.    -   Network Load Balancers: Load balancers distribute network        traffic across multiple servers or network links to optimize        resource usage, improve performance, and ensure high        availability of network services.    -   Network Print Servers: Print servers enable network printers to        be shared and accessed by multiple users within a network. They        manage print jobs, print queues, and provide print services to        network-connected devices.

These are just a few examples of networking devices commonly used incomputer networks. The combination and configuration of these devicesdepend on the specific requirements of the network and the desiredfunctionality.

Examples of COMPUTING DEVICES may include but are not limited toelectronic devices that process and manipulate data using computationalcapabilities. These devices are designed to perform various tasks,ranging from basic calculations to complex computations and dataprocessing. Computing devices come in different forms and sizes, eachtailored for specific purposes and user needs.

Examples of computing devices may include but are not limited to:

-   -   Personal Computers (PCs): Personal computers are general-purpose        computing devices designed for individual use. They consist of a        central processing unit (CPU), memory, storage devices,        input/output peripherals (keyboard, mouse, display), and an        operating system. PCs are versatile devices used for tasks such        as browsing the web, word processing, gaming, multimedia, and        more.    -   Laptops: Laptops are portable computing devices that provide the        same functionality as personal computers. They incorporate a        keyboard, display, and a built-in battery, allowing users to        work or perform tasks on the go.    -   Tablets: Tablets are lightweight, portable devices with        touchscreens and simplified user interfaces. They offer        functionality similar to laptops but with a more compact and        intuitive design. Tablets are commonly used for web browsing,        media consumption, e-books, and mobile applications.    -   Smartphones: Smartphones are mobile computing devices that        combine telephony capabilities with computing features. They        offer advanced functionality, including internet access, email,        multimedia, applications, and various sensors. Smartphones have        become an essential part of modern life, providing        communication, entertainment, and productivity features.    -   Servers: Servers are powerful computing devices designed to        manage and process vast amounts of data and provide services to        other devices or users. They are typically used in network        environments to store and deliver data, host websites and        applications, handle database management, and perform complex        computations.    -   Workstations: Workstations are high-performance computing        devices optimized for specialized tasks such as computer-aided        design (CAD), video editing, 3D rendering, scientific        simulations, and engineering. They typically have advanced        processing power, enhanced graphics capabilities, and extensive        memory capacity.    -   Embedded Systems: Embedded systems are specialized computing        devices embedded within other systems or products. They are        designed to perform specific functions and are often found in        automobiles, appliances, medical equipment, industrial        machinery, and other devices that require computing        capabilities.    -   Wearable Devices: Wearable devices are computing devices worn on        the body or integrated into clothing or accessories. Examples        include smartwatches, fitness trackers, augmented reality        glasses, and medical monitoring devices. These devices offer        features such as health tracking, notifications, communication,        and interaction with other devices.    -   Gaming Consoles: Gaming consoles are computing devices        specifically designed for playing video games. They provide        dedicated hardware and software platforms optimized for gaming,        often with advanced graphics processing capabilities.    -   Internet of Things (IoT) Devices: IoT devices are computing        devices embedded in everyday objects, connected to the internet,        and capable of collecting and exchanging data. Examples include        smart home devices, environmental sensors, industrial sensors,        and connected appliances.

These are just a few examples of computing devices, each servingdifferent purposes and catering to various computing needs. Thecomputing landscape is continually evolving, with new devices andtechnologies being developed to meet changing user requirements.

Examples of MANUFACTURING DEVICES (also known as industrialmanufacturing equipment) may include but are not limited to specializedmachines, tools, and systems used in the production and manufacturingprocesses across various industries. These devices are designed toautomate, optimize, and facilitate the manufacturing of products withefficiency, precision, and consistency. Manufacturing devices areemployed in sectors such as automotive, electronics, pharmaceuticals,food processing, textiles, and more.

Examples of manufacturing devices may include but are not limited to:

-   -   CNC Machines: Computer Numerical Control (CNC) machines are        automated machining tools that follow pre-programmed        instructions to shape and cut materials with high precision.        Examples include CNC milling machines, lathes, routers, and        laser cutting machines.    -   Robotics and Automation Systems: Robotic devices and automation        systems are used to automate repetitive tasks, assembly        processes, material handling, and packaging. Industrial robots        are programmable machines that perform tasks with speed,        accuracy, and consistency, improving productivity and reducing        human error.    -   Assembly Machines: These devices are specifically designed to        automate assembly processes by joining and fastening components        together. Examples include robotic arms, pick-and-place        machines, and specialized assembly line systems.    -   3D Printers: Also known as additive manufacturing machines, 3D        printers build three-dimensional objects by layering materials        based on digital models. They enable the rapid prototyping,        customization, and small-scale production of components or        products.    -   Industrial Sewing Machines: These machines are used in textile        and garment manufacturing to stitch fabrics and create finished        products such as clothing, upholstery, and accessories.        Industrial sewing machines offer enhanced speed, durability, and        specialized stitching capabilities.    -   Injection Molding Machines: Injection molding machines melt and        inject molten materials, typically plastics, into molds to        produce a wide range of products and components. They are used        in industries such as automotive, packaging, consumer goods, and        medical devices.    -   Packaging Machines: Packaging machines automate the process of        packaging products for distribution and sale. They can handle        tasks like filling, sealing, labeling, and palletizing. Examples        include form-fill-seal machines, blister packaging machines, and        cartoning machines.    -   Inspection and Quality Control Devices: These devices are used        to inspect and ensure the quality of manufactured products. They        include tools like coordinate measuring machines (CMM), vision        inspection systems, gauges, and sensors to detect defects,        measure dimensions, and verify product specifications.    -   Material Handling Equipment: Material handling devices such as        conveyor systems, automated guided vehicles (AGVs), forklifts,        and robotic arms facilitate the movement, storage, and        transportation of materials within the manufacturing facility.    -   Testing and Measurement Devices: Testing and measurement devices        are used to assess the performance, functionality, and quality        of manufactured products. Examples include hardness testers,        spectrometers, oscilloscopes, and gauges.

These are just a few examples of manufacturing devices, and the specificdevices used depend on the industry, production processes, and productrequirements. Manufacturing devices help streamline production, increaseefficiency, improve product quality, and reduce costs, contributing tothe overall success and competitiveness of manufacturing operations.

Examples of AGRICULTURAL DEVICES (also known as farm equipment oragricultural machinery) may include but are not limited to specializedtools, machines, and equipment designed to assist in various tasksrelated to agricultural practices. These devices are used by farmers andagricultural workers to automate, enhance efficiency, and improveproductivity in agricultural activities. Agricultural devices areutilized across different stages of farming, including land preparation,planting, cultivation, irrigation, harvesting, and post-harvestprocessing.

Examples of agricultural devices may include but are not limited to:

-   -   Tractors: Tractors are versatile vehicles used for multiple        farming tasks. They are equipped with powerful engines and        provide the necessary power and traction to perform tasks like        plowing, tilling, planting, hauling, and spraying. Tractors can        also be combined with various attachments and implements to        carry out specific tasks.    -   Harvesters: Harvesters are machines designed to efficiently        harvest crops such as grains, fruits, vegetables, and oilseeds.        Different types of harvesters exist for specific crops,        including combine harvesters for cereal crops, potato        harvesters, grape harvesters, and cotton pickers.    -   Planters and Seeders: Planters and seeders are devices used to        sow seeds in a controlled and efficient manner. They distribute        seeds evenly at precise depths and spacing, ensuring optimal        plant growth and yield. Planters and seeders can be manual,        animal-drawn, or tractor-mounted, depending on the scale of        farming operations.    -   Irrigation Systems: Irrigation devices are used to deliver water        to crops in a controlled manner, ensuring proper moisture levels        for growth. These systems include sprinklers, drip irrigation        systems, center pivot irrigation systems, and furrow irrigation        systems. They help conserve water, improve crop yield, and        reduce labor requirements.    -   Sprayers: Sprayers are used to apply fertilizers, pesticides,        herbicides, and other agricultural chemicals to crops. They can        be handheld, backpack-mounted, or tractor-mounted, equipped with        spray nozzles and tanks to evenly distribute the substances and        protect crops from pests, diseases, and weeds.    -   Plows and Tillage Equipment: Plows and tillage equipment are        used for primary tillage and land preparation. Plows break up        and turn over the soil, while tillage equipment further        cultivates the soil, preparing it for planting. Implements like        moldboard plows, disc harrows, and cultivators fall under this        category.    -   Livestock Equipment: Livestock equipment includes devices used        in animal husbandry and management. Examples include feeding        equipment, milking machines, animal handling systems, and barn        ventilation systems. These devices contribute to the care,        health, and productivity of livestock.    -   Grain Handling and Storage Equipment: Grain handling devices        such as grain elevators, grain dryers, and silos are used to        safely store, transport, and process harvested grains. They        facilitate efficient storage, drying, and handling of grains to        preserve their quality and prevent spoilage.    -   Hay and Forage Equipment: Hay and forage devices are used to        harvest, process, and store animal feed. They include equipment        such as hay balers, forage choppers, hay rakes, and bale        wrappers.    -   Post-Harvest Processing Equipment: Post-harvest processing        devices are used to clean, sort, grade, and process harvested        agricultural products. Examples include threshers, sorters,        graders, grain mills, and fruit and vegetable processing        equipment.

These are just a few examples of agricultural devices. The specificdevices used may vary depending on factors such as the type of crop,farming practices, scale of operations, and regional variations.Agricultural devices play a crucial role in modern farming, improvingefficiency, productivity, and sustainability in the agriculturalindustry.

Examples of ENERGY/REFINING DEVICES may include but are not limited tospecialized equipment and systems used in the energy industry,particularly in the refining and processing of various energy sources.These devices are crucial for extracting, converting, refining, anddistributing energy resources in different forms, such as oil, naturalgas, coal, and renewable energy sources. They are utilized in powerplants, refineries, and other energy production and distributionfacilities.

Examples of energy and refining devices may include but are not limitedto:

-   -   Refining Equipment: Refining devices are used in oil refineries        to process crude oil into various refined products such as        gasoline, diesel, jet fuel, lubricants, and other        petroleum-based products. Examples include distillation towers,        catalytic converters, hydrotreaters, and fluid catalytic        cracking units (FCCUs).    -   Boilers and Furnaces: Boilers and furnaces are devices used in        power plants and industrial facilities to generate steam or        heat. They burn fossil fuels or use other energy sources to        produce high-pressure steam that drives turbines and generates        electricity.    -   Turbines and Generators: Turbines, such as steam turbines, gas        turbines, and wind turbines, convert the kinetic energy of a        fluid or gas into mechanical energy. They are coupled with        generators to produce electrical energy. Turbines and generators        are key components in power generation systems.    -   Solar Panels: Solar panels, also known as photovoltaic panels,        convert sunlight into electrical energy. They consist of        interconnected solar cells that generate direct current (DC)        electricity when exposed to sunlight. Solar panels are used in        solar power systems to produce renewable energy.    -   Wind Turbines: Wind turbines capture the kinetic energy of the        wind and convert it into electrical energy. They consist of        large rotor blades that spin a generator when the wind blows.        Wind turbines are used in wind farms and off-grid applications        to generate clean and renewable energy.    -   Natural Gas Processing Equipment: Natural gas processing devices        are used to extract and process natural gas from its sources.        They include equipment such as compressors, separators,        dehydrators, and gas sweetening units. These devices remove        impurities and separate valuable components like methane,        ethane, propane, and butane.    -   Power Distribution Equipment: Power distribution devices include        transformers, switchgear, circuit breakers, and distribution        panels. They are used to control and distribute electrical        energy from power plants to various end-users, such as homes,        businesses, and industrial facilities.    -   Energy Storage Systems: Energy storage devices store excess        energy generated during periods of low demand and release it        during peak demand or when renewable energy sources are        unavailable. Examples include battery storage systems, pumped        storage hydropower, and compressed air energy storage (CAES)        systems.    -   Heat Exchangers: Heat exchangers transfer thermal energy between        two or more fluids at different temperatures. They are used in        various energy and refining processes to recover waste heat,        facilitate heat exchange, and improve energy efficiency.    -   Pipelines and Storage Tanks: Pipelines and storage tanks are        essential for transporting and storing energy resources like        oil, natural gas, and petroleum products. Pipelines transport        these resources over long distances, while storage tanks provide        temporary storage and distribution hubs.

These are just a few examples of energy and refining devices. The energyindustry is diverse, with a wide range of technologies and equipmentused to produce, refine, and distribute different forms of energy.Advances in technology and the growing focus on renewable energy sourcescontinue to drive innovation in this field.

Examples of AEROSPACE DEVICES may include but are not limited tospecialized equipment, systems, and vehicles used in the aerospaceindustry, which encompasses the design, development, production, andoperation of aircraft and spacecraft. These devices are designed toenable flight, exploration of space, and various aerospace-relatedactivities. They include a wide range of components, instruments, andsystems that are critical for aerospace operations.

Examples of aerospace devices may include but are not limited to:

-   -   Aircraft: Aircraft are vehicles designed to fly within the        Earth's atmosphere. They include various types such as        airplanes, helicopters, gliders, and unmanned aerial vehicles        (UAVs). Aircraft devices encompass airframes, engines, avionics        systems, landing gear, control surfaces, and onboard instruments        necessary for navigation, control, and communication.    -   Spacecraft: Spacecraft are vehicles designed for space        exploration and satellite deployment. They include crewed        spacecraft, such as capsules and space shuttles, as well as        robotic spacecraft, such as satellites, probes, and rovers.        Spacecraft devices include propulsion systems, life support        systems, communication systems, scientific instruments, solar        panels, and heat shields.    -   Rocket Engines: Rocket engines are used to propel spacecraft and        launch vehicles into space. They operate on the principle of        expelling high-speed exhaust gases to generate thrust. Rocket        engines include components such as combustion chambers, nozzles,        propellant tanks, and turbopumps.    -   Avionics Systems: Avionics systems refer to the electronic        systems used in aircraft for navigation, communication, flight        control, and monitoring. They include devices such as flight        computers, navigation systems (GPS), radar systems,        communication systems, autopilots, and cockpit displays.    -   Aircraft Engines: Aircraft engines provide the necessary thrust        to propel aircraft through the air. They include various types        such as turbojet engines, turboprop engines, turbofan engines,        and turboshaft engines. Aircraft engines are complex devices        comprising components such as combustion chambers, turbines,        compressors, and fuel systems.    -   Control Systems: Aerospace control systems are crucial for        maneuvering, stability, and control of aircraft and spacecraft.        They include flight control surfaces, such as ailerons,        elevators, and rudders, as well as systems like fly-by-wire,        autopilots, and attitude control thrusters for spacecraft.    -   Satellite Systems: Satellite systems consist of components and        devices used for communication, navigation, remote sensing, and        scientific research. They include satellite buses (platforms),        payloads (instruments), antennas, solar panels, attitude control        systems, and telemetry systems.    -   Parachutes: Parachutes are devices used for deceleration and        landing of aircraft, spacecraft, or payloads. They are crucial        for safe re-entry and recovery of crewed spacecraft, as well as        for cargo or personnel airdrops.    -   Ground Support Equipment: Ground support equipment refers to the        devices used on the ground to support aerospace operations.        Examples include aircraft ground handling equipment, such as        tugs, loaders, and fueling systems, as well as launch pad        equipment for spacecraft, such as umbilical towers, gantries,        and fueling systems.    -   Flight Simulators: Flight simulators are devices used for pilot        training, aircraft system testing, and research. They provide a        simulated environment that replicates the experience of flying        an aircraft, including the cockpit controls, instruments, and        visual displays.

These are just a few examples of aerospace devices, and the aerospaceindustry encompasses a vast array of technologies and equipment. Thedevelopment and utilization of these devices enable advancements inaviation, space exploration, satellite communication, and scientificresearch.

Examples of FORESTRY DEVICES may include but are not limited tospecialized tools, equipment, and machinery used in the field offorestry for various tasks related to the management, harvesting, andprocessing of trees and forests. These devices are designed to improveefficiency, safety, and productivity in forestry operations. They areused by foresters, loggers, and other professionals involved in forestmanagement and timber production.

Examples of forestry devices may include but are not limited to:

-   -   Chainsaws: Chainsaws are portable mechanical saws powered by        either electricity, gasoline, or battery. They are used for        felling trees, limbing, bucking (cutting felled trees into        logs), and other tree-cutting operations in the forest.    -   Harvesters: Harvesters are specialized forestry machines        designed for felling, delimbing, and processing trees in a        single operation. They can fell, strip branches, and cut trees        into logs, significantly reducing manual labor and increasing        productivity.    -   Forwarders: Forwarders are purpose-built vehicles used to        transport logs and other forest products from the cutting site        to a central location, typically a log landing or a roadside        collection point. They have a loading area and a crane for        lifting and loading logs onto the vehicle.    -   Skidders: Skidders are heavy-duty machines used to extract logs        from the forest and drag them to a landing or a loading area.        They have large grapple arms or winches to grip and lift logs        for transportation.    -   Logging Trucks: Logging trucks are specialized trucks used to        transport logs from the forest to sawmills or other processing        facilities. They are designed with trailers and secure        load-holding structures to transport logs safely and        efficiently.    -   Mulchers: Mulchers are machines used to clear vegetation,        shrubs, and small trees in forestry operations. They are        equipped with rotating blades or hammers that shred vegetation,        enabling land clearing and site preparation.    -   Portable Sawmills: Portable sawmills are compact and        transportable machines used for on-site processing of logs into        lumber. They allow for immediate sawing of felled trees,        reducing transportation costs and time to the sawmill.    -   Chippers: Chippers are devices used to process tree branches,        limbs, and other forestry residues into wood chips. Wood chips        are used for various purposes, including fuel, landscaping, and        the production of pulp and paper.    -   Tree Planters: Tree planters are devices used for efficient tree        planting in reforestation and afforestation projects. They can        dig holes, place seedlings, and cover them with soil, improving        the speed and accuracy of tree planting operations.    -   Forest Firefighting Equipment: Forest firefighting equipment        includes devices like fire pumps, hoses, and fire suppression        tools used to combat and control forest fires. They are crucial        for protecting forests and minimizing the damage caused by        wildfires.

These are just a few examples of forestry devices, and the specificdevices used may vary depending on factors such as the type of forestryoperation, terrain, tree species, and regional practices. Forestrydevices play a vital role in sustainable forest management, timberproduction, and environmental conservation.

Examples of DEFENSE DEVICES (also known as military devices or weaponssystems) may include but are not limited to specialized equipment,technologies, and systems designed and utilized by military forces fordefense and security purposes. These devices are designed to protect acountry's interests, deter potential threats, and ensure the safety ofmilitary personnel and civilians. Defense devices encompass a wide rangeof technologies and equipment used for various defense applications.

Examples of defense devices may include but are not limited to:

-   -   Firearms: Firearms include various handheld weapons designed to        launch projectiles using the force of expanding high-pressure        gases. They encompass rifles, pistols, machine guns, and        shotguns, which are used by military personnel for individual        combat or close-quarters engagements.    -   Artillery: Artillery devices are heavy guns or cannons used for        long-range indirect fire support. They can fire explosive        projectiles or provide suppressive fire. Examples include        howitzers, mortars, and rocket launchers.    -   Missiles: Missiles are self-propelled weapons that can be guided        to specific targets. They can be launched from ground-based        systems, ships, submarines, aircraft, or launched from portable        platforms. Missiles include surface-to-air missiles (SAMs),        surface-to-surface missiles (SSMs), anti-ship missiles, and        air-to-air missiles (AAMs).    -   Tanks: Tanks are heavily armored tracked vehicles equipped with        powerful cannons. They are used for ground combat and provide        offensive and defensive capabilities on the battlefield. Tanks        combine firepower, mobility, and protection.    -   Fighter Aircraft: Fighter aircraft are high-performance military        aircraft designed for air-to-air combat and ground attack        missions. They are equipped with advanced avionics, radar        systems, missiles, and guns for air superiority and tactical        strikes.    -   Warships: Warships include naval vessels designed for combat        operations at sea. They range from aircraft carriers,        destroyers, and frigates to submarines and patrol boats.        Warships are equipped with various weapon systems, including        missiles, naval guns, torpedoes, and anti-aircraft systems.    -   Unmanned Aerial Vehicles (UAVs): UAVs, also known as drones, are        remotely piloted or autonomous aircraft used for reconnaissance,        surveillance, and targeted strikes. They provide real-time        intelligence and can be armed with missiles or bombs.    -   Electronic Warfare Systems: Electronic warfare devices encompass        a range of systems used to detect, deceive, disrupt, and counter        enemy electronic systems. They include radar jammers, signal        intelligence equipment, electronic countermeasures, and        defensive systems to protect against cyber threats.    -   Ballistic Missile Defense Systems: Ballistic missile defense        devices are designed to detect, track, and intercept incoming        ballistic missiles. These systems employ sensors, radars,        interceptor missiles, and command and control systems to protect        against missile threats.    -   Protective Gear: Protective gear includes devices such as body        armor, helmets, gas masks, and protective clothing worn by        military personnel to provide protection against physical,        ballistic, and chemical threats in combat situations.

These are just a few examples of defense devices. The defense industryis highly advanced and continuously evolving, driven by technologicaladvancements and strategic needs. It encompasses a vast array of devicesand systems tailored to meet the specific requirements of modernmilitary forces.

Information process 10 may normalize 104 the data signals (e.g., datasignals 200 and/or data signals 204) to generate a plurality ofhomogenized signals (e.g., data signals 200′ and/or data signals 204′)so that the data signals (e.g., data signals 200 and/or data signals204) can work together.

For example and when normalizing data, information process 10 maytransform data into a standardized format or range, which may involveadjusting the values of a dataset to a common scale, typically between 0and 1 or -1 and 1, wherein the goal of data normalization is toeliminate the effects of varying scales, units, or distributions withinthe data, allowing for fairer comparisons and more accurate analysis.

Normalization is particularly useful when working with datasets thathave different measurement units or widely varying ranges. By bringingall the data to a common scale, normalization enables meaningfulcomparisons and helps algorithms or models to better interpret andprocess the data, as it may prevent certain features from dominating theanalysis or introducing bias due to their larger magnitude.

Examples of methods of normalizing data may include but are not limitedto:

-   -   Min-Max Normalization (also known as feature scaling): This        method scales the data linearly to a specific range, often        between 0 and 1. It involves subtracting the minimum value of        the feature and then dividing by the range (i.e., the difference        between the maximum and minimum values). The formula for Min-Max        normalization is: normalized_value=(x−min(x))/(max(x)−min(x))    -   Z-Score Normalization (also known as standardization): This        method transforms the data to have a mean of 0 and a standard        deviation of 1. It involves subtracting the mean value of the        feature and dividing by the standard deviation. The formula for        Z-Score normalization is:        normalized_value=(x−mean(x))/standard_deviation(x)    -   Decimal Scaling: In this method, the data is scaled by shifting        the decimal point of each value. The number of decimal places to        shift is determined based on the maximum absolute value in the        dataset.

For example and when normalizing 104 the data signals (e.g., datasignals 200 and/or data signals 204) to generate a plurality ofhomogenized signals (e.g., data signals 200′ and/or data signals 204′)so that the data signals (e.g., data signals 200 and/or data signals204) can work together, information process 10 may: rescale 106 the datasignals (e.g., data signals 200 and/or data signals 204); and/or rebase108 the data signals (e.g., data signals 200 and/or data signals 204).

Rescaling data refers to the process of changing the scale or range ofvalues in a dataset without necessarily transforming them into aspecific standardized format. Unlike normalization, which typically aimsto bring the data into a common scale, rescaling allows for adjustmentsthat can be tailored to specific requirements or preferences. The goalof rescaling data is to manipulate the values in a way that preservesthe relationships and distribution of the original data while fittingthem into a desired range or scale. This can be useful for variousreasons, such as enhancing visualization, improving algorithmperformance, or accommodating specific constraints or preferences.

Examples of rescaling methods may include but are not limited to:

-   -   Min-Max Rescaling: Similar to min-max normalization, min-max        rescaling scales the data to a specific range, often between 0        and 1 or any other desired minimum and maximum values. It        involves subtracting the minimum value of the feature and then        dividing by the range (i.e., the difference between the maximum        and minimum values). The formula for min-max rescaling is the        same as in normalization:        rescaled_value=(x−min(x))/(max(x)−min(x))    -   Feature Scaling: Feature scaling rescales each feature (column)        in a dataset independently, without considering the range of the        entire dataset. It can be done using various methods, such as        standardization (Z-score normalization), range scaling, or        decimal scaling.    -   Logarithmic Rescaling: Logarithmic rescaling involves applying a        logarithmic function to the data values. This transformation can        compress the scale of large values while expanding the scale of        small values. Logarithmic rescaling is often useful when dealing        with data that spans several orders of magnitude or has a skewed        distribution.    -   Power Rescaling: Power rescaling applies a power function to the        data values. It can be useful for adjusting the scale of values        that are disproportionately large or small. By raising the        values to a power, such as squaring or taking the square root,        the scale can be modified accordingly.

Rescaling data allows for flexible adjustments to meet specific needs orpreferences. However, it's important to note that rescaling does notnecessarily eliminate the differences in distribution or units ofmeasurement among features. The choice of rescaling method should bebased on the characteristics of the data and the objectives of theanalysis or modeling task.

Rebasing data refers to the process of recalculating or adjusting thevalues of a dataset with respect to a new base or reference point. Itinvolves shifting the entire dataset by a certain amount or percentageto establish a different baseline or starting point for the data. Thepurpose of rebasing data is often to facilitate comparisons, identifytrends, or analyze changes relative to a specific reference point. Byrebasing the data, you can normalize it with respect to a chosen baseand evaluate the relative changes or growth rates in the values.

The rebasing process may involve the following steps:

-   -   Selecting a Base Period: Choose a specific time period or        reference point that will serve as the new base or starting        point for the data. This period is often set to a specific date,        such as the beginning of a year or a particular milestone.    -   Calculating the Rebased Values: Subtract or adjust the original        values of the dataset by the difference between the chosen base        period and the original base period. This adjustment aligns the        data with the new base period and establishes the rebased        values.    -   Expressing Rebased Values: Express the rebased values as indices        or ratios relative to the base period. For example, if the base        period has a rebased value of 100, other periods' values will be        expressed relative to that base (e.g., 105 means a 5% increase        from the base).

Rebasing can be useful in various scenarios, such as financial analysis,economic indicators, or market indices. It allows for a clearerunderstanding of relative changes over time and facilitates comparisonsacross different periods or entities.

As discussed above, information process 10 may be utilized to functionas an intermediary between devices that are offered by multiple vendors,wherein information process 10 may be configured to effectuatecommunication between devices produced by different vendors. Accordinglyand by performing the operations discussed aboe (e.g., normalizing,rescaling, rebasing), the various devices can now exchange information.Accordingly, information in the form of homogenized signals (e.g., datasignals 200′ and/or data signals 204′) may be exchanged: between devices(e.g., first vendor devices 202 and/or second vendor devices 206), fromdevices (e.g., first vendor devices 202 and/or second vendor devices206) to information process 10, and from information process 10 todevices (e.g., first vendor devices 202 and/or second vendor devices206); thus enabling the free exchange of information/data, the remotecontrol of such devices (e.g., first vendor devices 202 and/or secondvendor devices 206), the remote adjustment of such devices (e.g., firstvendor devices 202 and/or second vendor devices 206), and the remoteconfiguration of such devices (e.g., first vendor devices 202 and/orsecond vendor devices 206).

Information process 10 may provide 110 the plurality of homogenizedsignals (e.g., data signals 200′ and/or data signals 204′) to a postprocessing system (e.g., post processing system 218).

A post-processing system (e.g., post processing system 218) refers to aset of activities, tools, or techniques that are applied to data oroutput after an initial process or operation has taken place. Itinvolves analyzing, refining, and enhancing the data or results obtainedfrom a primary process to derive additional insights or improve thequality and usability of the output.

Post-processing systems (e.g., post processing system 218) are commonlyused in various fields, including scientific research, engineering,computer graphics, data analysis, and more. They are designed to performtasks such as data filtering, noise reduction, data visualization, dataintegration, feature extraction, data transformation, and resultinterpretation.

Examples of post-processing systems may include but are not limited to:

-   -   Image and Video Processing: In image and video processing,        post-processing systems are employed to enhance the quality,        remove noise or artifacts, adjust brightness or contrast, apply        filters or effects, and perform image or video stabilization.        These systems help to improve visual perception, extract        meaningful information, or prepare the data for further analysis        or presentation.    -   Signal Processing: Post-processing systems in signal processing        deal with analyzing and modifying signals obtained from various        sources. They can involve techniques like noise filtering,        frequency analysis, feature extraction, signal denoising, signal        reconstruction, or signal normalization. These systems help to        improve the accuracy, reliability, or interpretability of        signals.    -   Computational Modeling and Simulation: Post-processing systems        are used to analyze and interpret the results obtained from        computational models and simulations. They involve tasks like        data visualization, data analysis, statistical analysis,        identifying trends or patterns, and extracting meaningful        insights from the simulation outputs. These systems aid in        understanding the behavior, performance, or impact of the        modeled system or phenomenon.    -   Data Analysis and Machine Learning: In data analysis and machine        learning, post-processing systems are employed to refine and        interpret the results obtained from data mining, statistical        analysis, or machine learning algorithms. They can involve tasks        such as data visualization, outlier detection, error correction,        feature selection, result validation, or model interpretation.        These systems help to extract valuable knowledge, validate the        findings, or make the results more understandable and        actionable.    -   Natural Language Processing: Post-processing systems in natural        language processing deal with refining and improving the output        generated by language processing algorithms. They can involve        tasks like grammatical error correction, language translation,        sentiment analysis, information extraction, or summarization.        These systems aim to enhance the accuracy, fluency, or coherence        of the processed text.

Overall, post-processing systems (e.g., post processing system 218) playa crucial role in refining, enhancing, and interpreting the resultsobtained from various processes or algorithms. They contribute toimproving the quality, usability, and understanding of the data oroutput, leading to more meaningful insights and informeddecision-making.

Information process 10 may provide 112 the plurality of homogenizedsignals (e.g., data signals 200′ and/or data signals 204′) to a displaysystem (e.g., display system 220).

A display system (e.g., display system 220) refers to a combination ofhardware and software components designed to present visual informationor output to users. It encompasses various devices and technologies usedto display images, text, graphics, videos, or other visual content forhuman perception.

Display systems (e.g., display system 220) are widely used in a varietyof applications, including computer systems, consumer electronics,entertainment, information display, medical imaging, advertising, andmore. They provide a means to visually communicate information, enhanceuser experience, and facilitate interaction with digital content.

Examples of display systems may include but are not limited to:

-   -   Monitors: Monitors are the most common type of display system        used in computers, laptops, and other electronic devices. They        typically use liquid crystal display (LCD), light-emitting diode        (LED), or organic light-emitting diode (OLED) technologies to        present visual content on a flat screen.    -   Projectors: Projectors are display systems that project images        or video onto a large screen or surface. They use light sources        and optical systems to enlarge and project the content onto a        surface for viewing by a larger audience. Projectors are        commonly used in classrooms, conference rooms, theaters, and        home entertainment systems.    -   Televisions: Televisions (TVs) are display systems specifically        designed for broadcasting television programs and other video        content. They come in various sizes and technologies, such as        LCD, LED, OLED, or plasma, and often include additional features        like smart capabilities and connectivity options.    -   Head-Mounted Displays (HMDs): HMDs are wearable display systems        that immerse the user in a virtual or augmented reality        environment. They typically consist of a headset or glasses with        integrated display screens, sensors, and audio systems. HMDs are        used in gaming, simulations, training, and other immersive        experiences.    -   Digital Signage: Digital signage refers to display systems used        for advertising, information dissemination, or wayfinding in        public spaces, retail stores, transportation hubs, and other        locations. These systems typically consist of large display        panels or screens that can present dynamic content, including        text, images, videos, and interactive elements.    -   Touchscreens: Touchscreen displays combine visual output with        interactive input capabilities. They allow users to interact        with the displayed content by directly touching the screen.        Touchscreens are used in smartphones, tablets, kiosks,        interactive displays, and other devices that require user input.    -   Wearable Displays: Wearable displays are integrated into        wearable devices like smartwatches, fitness trackers, and smart        glasses. They provide users with visual feedback, notifications,        and information in a compact and portable form factor.

Display systems (e.g., display system 220) may also include additionalfeatures such as high-definition (HD) or 4K resolution, high refreshrates for smooth motion, color calibration, adjustable settings, andconnectivity options to connect to various devices or networks.

Overall, display systems (e.g., display system 220) are essentialcomponents of modern technology, enabling the visual presentation ofinformation and content in various applications, from personal devicesto large-scale displays for public viewing.

Information process 10 may provide 114 the plurality of homogenizedsignals (e.g., data signals 200′ and/or data signals 204′) to anotification system (e.g., notification system 222).

A notification system (e.g., notification system 222) refers to a set ofprocesses, tools, and technologies used to deliver alerts, messages, orupdates to users or recipients. It enables the dissemination ofinformation in a timely manner, ensuring that individuals are promptlynotified about important events, changes, or actions that require theirattention.

Notification systems (e.g., notification system 222) are commonly usedin a wide range of contexts, including communication platforms, mobileapplications, web services, enterprise systems, and more. They provide ameans to notify users about various types of events, such as newmessages, system status updates, reminders, alarms, security alerts, orworkflow notifications.

Some key components and features of a notification system may includebut are not limited to:

-   -   Trigger: A notification system is triggered by a specific event        or condition that requires user awareness or action. Triggers        can include incoming messages, updates to a system, time-based        events, user interactions, data changes, or predefined rules.    -   Delivery Channels: Notification systems utilize various delivery        channels to reach users effectively. These channels can include        mobile push notifications, email, SMS text messages, in-app        messages, pop-up alerts, browser notifications, voice calls, or        even physical devices like pagers or smartwatches.    -   Personalization and Targeting: Notification systems often allow        for personalization and targeting of notifications to specific        users or user groups. This ensures that notifications are        relevant to the recipient's preferences, interests, or context,        increasing their effectiveness and reducing unnecessary noise.    -   Prioritization and Urgency: Notifications can be prioritized        based on their importance or urgency. Critical alerts may        require immediate attention, while less important notifications        can be scheduled or displayed in a less intrusive manner.    -   Customization and Preferences: Users often have the ability to        customize their notification preferences, including the types of        events they want to be notified about, the delivery channels        they prefer, and the frequency or timing of notifications.        Customization options help users tailor the notification system        to their specific needs and avoid notification overload.    -   Logging and History: Notification systems may maintain a log or        history of sent notifications for reference or auditing        purposes. This can include details such as the content, delivery        time, recipient, and status of each notification.    -   Feedback and Interaction: Some notification systems allow users        to interact with notifications, providing options to        acknowledge, dismiss, or take action directly from the        notification itself. This enhances user engagement and        facilitates seamless workflows.

Notification systems (e.g., notification system 222) play a crucial rolein keeping users informed, engaged, and up-to-date with relevantinformation. They are utilized in various domains, including messagingapps, social media platforms, customer support systems, ITinfrastructure monitoring, task management tools, and more. Theeffectiveness of a notification system depends on careful design,appropriate targeting, and respect for user preferences and privacy.

Patient Onboarding Process to Establish Patient Norms:

The following discussion concerns the manner in which informationprocess 10 may be utilized to establish norms for a patient whileonboarding the patient within a hospital. As is often the case, when apatient is initially connected to e.g., various monitoring deviceswithin a hospital room, it may be initially unclear as to where apatient's vital signs should be (e.g., What is their normal heart rate?What is their normal respiratory rate? What is their normal bloodpressure? etc.). Accordingly and as will be discussed below, informationprocess 10 may be configured to streamline such an onboarding process.

Referring also to FIG. 4 , information process 10 may monitor 300 adevice (e.g., one or more of first vendor devices 202 and/or one or moreof second vendor devices 206) to receive data signals (e.g., one or moreof data signals 200 and/or one or more of data signals 204) indicativeof the device (e.g., one or more of first vendor devices 202 and/or oneor more of second vendor devices 206).

The data signals (e.g., one or more of data signals 200 and/or one ormore of data signals 204) may concern one or more details of the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) and/or uses of the device (e.g., one or moreof first vendor devices 202 and/or one or more of second vendor devices206).

-   -   Device Details: One or more details of the device (e.g., one or        more of first vendor devices 202 and/or one or more of second        vendor devices 206) may concern one or more readings, signals        and/or alarms that are provided by the device and concern (in        the example) the vital signs of a patient.    -   Device Uses: One or more uses of the device (e.g., one or more        of first vendor devices 202 and/or one or more of second vendor        devices 206) may concern the manner in which the device is being        used (e.g., what is the device doing, what is the device being        used for, who is the device assigned/connected to, etc.).

As discussed above, the device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206) may includeone or more of: a medical device, a process control device, a networkingdevice, a computing device, a manufacturing device, an agriculturaldevice, an energy/refining device, an aerospace device, a forestrydevice, and a defense device.

One or more of the devices (e.g., one or more of first vendor devices202 and/or one or more of second vendor devices 206) may include one ormore sub devices (e.g., sub devices 224, 226). Examples of such subdevices (e.g., sub devices 224, 226) may include any subordinate devicethat depends upon and/or interacts with a superior device. For example,a subordinate wireless blood gas monitor (e.g., sub device 224) and/or asubordinate wireless heart rate monitor (e.g., sub devices 226) maydepend upon and/or interact with superior client vital sign monitoringdevice (e.g., first vendor device 202).

Information process 10 may compare 302 the data signals (e.g., one ormore of data signals 200 and/or one or more of data signals 204) todefined signal norms (e.g., defined signal norms 228) to identifyoutliers (e.g., outliers 230).

In statistics, an outlier (e.g., outliers 230) is an observation or datapoint that significantly deviates from the other observations in adataset. It is a value that lies an abnormal distance away from otherdata points and may be indicative of a rare or unusual occurrence,measurement error, or data entry mistake. Outliers can arise due tovarious reasons, such as natural variability, measurement errors, datacorruption, or extreme events. Outliers can have a disproportionateimpact on statistical analyses, leading to skewed results or inaccurateconclusions if not properly handled. Identifying and handling outliersis an important step in data analysis and statistical modeling. Outlierscan be detected through various methods, including graphical techniques(e.g., scatter plots, box plots) or statistical tests (e.g., z-scores,modified z-scores, Mahalanobis distance).

For example, assume that the data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204) concern the heartrate and respiratory rate of a patient (e.g., patient 232). Further,assuming that the patient (e.g., patient 232) is a 50-year-old male ofaverage health, the defined signal norms (e.g., defined signal norms228) would be a heart rate of 60-100 beats per minute and a respiratoryrate of 12-20 breaths per minute. Accordingly, an outlier (e.g.,outliers 230) may be a data signal (e.g., one or more of data signals200 and/or one or more of data signals 204) that is above or below thesedefined signal norms (e.g., defined signal norms 228). So a heart rateof <60 beats per minute or >100 beats per minute may be consideredoutliers (e.g., outliers 230). Further, a respiratory rate of <12breaths per minute or >20 breaths per minute may be considered outliers(e.g., outliers 230).

Additionally/alternatively, such defined signal norms (e.g., definedsignal norms 228) may be more bespoke and individualized. So while thedefined signal norms (e.g., defined signal norms 228) for a heart rateis 60-100 beats per minute and a respiratory rate is 12-20 breaths perminute; if the patient (e.g., patient 232) is a seasoned athlete ofexceptional health, their “normal” heartrate may be 50-55 beats perminute and their “normal” respiratory rate may be 9-11 breaths perminute. Accordingly and in such a situation, the individual “norms” ofthe patient (e.g., patient 232) would consistently be outliers (e.g.,outliers 230) if the societal heartrate norms and respiratory rate normswere applied to patient 232.

These defined signal norms (e.g., defined signal norms 228) may includeuser-defined signal norms and/or machine-defined signal norms. Forexample and with respect to user-defined signal norms, such user-definedsignal norms may be the result of (in this example) medical studies,medical books, insurance charts, medical records, etc. Further and withrespect to machine-defined signal norms, such machine-defined signalnorms may be defined via massive data sets that are processed by machinelearning.

As is known in the art, a MASSIVE DATASET, also referred to as alarge-scale dataset or big dataset, is a collection of data that isexceptionally large in size and complexity. These datasets typicallyexceed the capacity of traditional data processing and analysis tools,requiring specialized approaches and infrastructure to handle andextract insights from them effectively.

The term “massive” is relative and can vary depending on the context andavailable resources. The size of a massive dataset can range fromterabytes (10¹² bytes) to petabytes (10¹⁵ bytes) or even exabytes (10¹⁸bytes) and beyond. Massive datasets can arise from various sources anddomains, including scientific research, social media, e-commerce,financial transactions, sensor networks, genomics, astronomy, and more.They often contain a high volume of records, measurements, orobservations, along with diverse data types such as text, images,videos, time series, graphs, or unstructured data.

Working with massive datasets poses several challenges, includingstorage, processing, analysis, and scalability. Traditional methods andtools may not be sufficient to handle these datasets efficiently.Specialized technologies and techniques, such as distributed computing,parallel processing, cloud computing, and big data frameworks (e.g.,Apache Hadoop, Apache Spark), are often employed to manage and processthe data at scale.

The analysis of massive datasets aims to extract meaningful insights,patterns, correlations, or trends from the vast amount of availabledata. This process involves data preprocessing, cleansing,transformation, statistical analysis, machine learning, datavisualization, and other techniques tailored to handle the specificchallenges of large-scale data. The insights derived from massivedatasets can have significant implications in various domains, includingscientific discoveries, business intelligence, personalizedrecommendations, predictive analytics, fraud detection, andinfrastructure optimization. It's worth noting that the term “massivedataset” is often used interchangeably with terms like “big data” or“large-scale data.” While there is no strict definition for these terms,they generally refer to datasets that exceed the capabilities ofconventional data processing methods and require specialized approachesfor storage, management, and analysis.

As is known in the art, MACHINE LEARNING is a subfield of artificialintelligence (AI) that focuses on the development of algorithms andmodels that enable computers to learn from and make predictions ordecisions based on data, without being explicitly programmed. Itinvolves the use of statistical techniques and computational algorithmsto identify patterns, extract insights, and make predictions ordecisions from the available data.

Machine learning algorithms are designed to automatically learn andimprove from experience or examples, allowing them to adapt to new dataand make accurate predictions or decisions. These algorithms can bebroadly categorized into three main types:

-   -   Supervised Learning: In supervised learning, the machine        learning algorithm learns from a labeled dataset, where each        data instance is associated with a known target or outcome. The        algorithm learns to generalize from the labeled examples and        make predictions on new, unseen data. Examples of supervised        learning algorithms include linear regression, decision trees,        support vector machines (SVM), and neural networks.    -   Unsupervised Learning: In unsupervised learning, the machine        learning algorithm explores the underlying structure or patterns        in the dataset without explicit labels or targets. It aims to        discover hidden patterns, clusters, or associations in the data.        Unsupervised learning algorithms include clustering algorithms        (e.g., k-means, hierarchical clustering) and dimensionality        reduction techniques (e.g., principal component analysis,        t-SNE).    -   Reinforcement Learning: Reinforcement learning involves an agent        that learns to interact with an environment and make decisions        based on trial and error. The agent learns through feedback in        the form of rewards or penalties, guiding it to optimize its        actions and maximize its cumulative reward over time.        Reinforcement learning algorithms are commonly used in robotics,        gaming, and control systems.

Machine learning algorithms and models play a crucial role in processingmassive datasets. As datasets grow in size, traditional data processingand analysis methods may become impractical or infeasible. Machinelearning offers scalable and automated approaches to handle and extractinsights from massive datasets.

Machine learning algorithms can handle large-scale datasets byleveraging distributed computing and parallel processing techniques.Technologies like Apache Spark, Hadoop, and GPU acceleration enable theefficient processing and analysis of massive datasets. Machine learningmodels can be trained on subsets of the data in parallel or distributedacross multiple computing resources to accelerate the learning process.Furthermore, machine learning techniques are designed to identifypatterns, relationships, and dependencies in the data, allowing them tocapture complex interactions and make predictions or decisions based onthe patterns learned from the massive dataset. By learning from thedata, machine learning models can handle the high dimensionality,variability, and complexity often present in massive datasets.

Such defined signal norms (e.g., defined signal norms 228) may becompartmentalized by e.g., gender, race, age, location, device type,device class, seasonality, time of day, etc. Specifically, medicalstatistics may vary depending upon various factors (including gender,race, age, location, device type, device class, seasonality, and time ofday), wherein these factors can influence health outcomes, diseaseprevalence, treatment response, and other medical parameters.

For example and with respect to such factors:

-   -   Gender: Biological differences between males and females can        lead to variations in health conditions, disease incidence,        treatment responses, and outcomes. For example, certain diseases        or conditions may predominantly affect one gender more than the        other.    -   Race: Different racial and ethnic groups can exhibit variations        in disease prevalence, genetic factors, response to treatments,        and healthcare disparities. These differences can contribute to        variations in medical statistics among different racial and        ethnic populations.    -   Age: Medical statistics often vary across different age groups.        Certain diseases or conditions may be more common or have        different manifestations in specific age brackets, such as        pediatric or geriatric populations.    -   Location: Geographical location can impact medical statistics        due to differences in environmental factors, access to        healthcare, lifestyle choices, genetic variations, and regional        disease patterns. For example, certain diseases may be more        prevalent in specific regions or countries.    -   Device Type and Device Class: In medical research and        statistics, different types and classes of devices can have        varying performance, efficacy, safety profiles, and outcomes.        The characteristics and use of specific medical devices can        influence medical statistics related to their effectiveness,        complications, and patient outcomes.    -   Seasonality: Some medical conditions or diseases exhibit        seasonal patterns. For instance, respiratory illnesses like        influenza may be more prevalent during certain seasons. Seasonal        variations can affect medical statistics related to disease        incidence, hospitalizations, and mortality rates.    -   Time of Day: Physiological parameters and disease symptoms can        vary throughout the day. For example, blood pressure and heart        rate can fluctuate depending on circadian rhythms. Time of day        can influence medical statistics related to monitoring vital        signs or evaluating symptoms at different time points.

Further, this list of factors is not intended to be exhaustive, andthere may be other factors specific to certain medical conditions orstudies that can contribute to variations in medical statistics.Additionally, there may be a historical component to such defined signalnorms (e.g., defined signal norms 228), wherein historical norms acrossdifferent recent timespans have varying implications (i.e. last 15 minsvs. last 6 hours). For example, the historical norm for a patientadmitted for extreme hypertension may have a very highsystolic/diastolic pressure readings when the patient is first admitted. . . but may have much lower systolic/diastolic pressure readings thefollowing day or week.

Information process 10 may investigate 304 the outliers (e.g., outliers230) to determine if an issue exists with the device (e.g., one or moreof first vendor devices 202 and/or one or more of second vendor devices206). For example, information process 10 may investigate 304 theoutliers (e.g., outliers 230) to determine if: the outliers (e.g.,outliers 230) are inaccurate (e.g., due to a malfunctioning device); theoutliers (e.g., outliers 230) are accurate but the patient (e.g.,patient 232) is not experiencing an issue (e.g., due to the patients“norms” being outside of societal “norms”); and the outliers (e.g.,outliers 230) are accurate and the patient (e.g., patient 232) isexperiencing an issue.

For example and when investigating 304 the outliers (e.g., outliers 230)to determine if an issue exists with the device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206), information process 10 may physically investigate 306 the outliers(e.g., outliers 230). For example, information process 10 may requestthat e.g., a nurse assigned to patient 232 physically investigate 306the outliers (e.g., outliers 230) by visiting the room of patient 232 toe.g., confirm the proper operation of the device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206) and/or to confirm that the patient (e.g., patient 232) is notexperiencing a medical issue (e.g., a low/high heart rate and/or alow/high respiratory rate).

Additionally/alternatively and when investigating 304 the outliers(e.g., outliers 230) to determine if an issue exists with the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206), information process 10 may examine 308 otherdata signals from the device (e.g., one or more of first vendor devices202 and/or one or more of second vendor devices 206). Often times, whena patient is experiencing a medical issue, multiple events may occur.For example, if the patient (e.g., patient 232) is experiencing arespiratory medical issue, a reduced heart rate may be accompanied by anelevated respiratory rate or a reduced blood gas saturation. So in theevent that the outlier for patient 232 is a reduced heart rate,information process 10 may examine 308 other data signals (e.g.,respiratory rate and/or blood gas saturation) from the device (e.g., oneor more of first vendor devices 202 and/or one or more of second vendordevices 206) to determine if an issue exists. Therefore, if the otherdata signals (e.g., respiratory rate and/or blood gas saturation) fromthe device are normal, information process 10 may determine that anissue does not exist (e.g., patient 322 is not having a medical issue).

Information process 10 may adjust 310 outlier definition criteria toeliminate the outlier (e.g., outliers 230) if an issue does not exist.As discussed above, if the patient (e.g., patient 232) is a seasonedathlete of exceptional health, their “normal” heartrate may be 50-55beats per minute and their “normal” respiratory rate may be 9-11 breathsper minute. Accordingly and in such a situation, the individual “norms”of the patient (e.g., patient 232) would consistently be outliers (e.g.,outliers 230) if the societal heartrate norms and respiratory rate normswere applied to patient 232. Accordingly, information process 10 mayadjust 310 outlier definition criteria to eliminate the outlier (e.g.,outliers 230) if an issue does not exist, wherein this outlierdefinition criteria may include signal thresholds.

As discussed above, while the defined signal norms (e.g., defined signalnorms 228) for a heart rate is 60-100 beats per minute and a respiratoryrate is 12-20 breaths per minute, information process 10 may adjust 310outlier definition criteria to eliminate the outlier (e.g., outliers230) if an issue does not exist.

For example and when adjusting 310 the outlier definition criteria,information process 10 may define 312 bespoke outlier definitioncriteria for the device (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206). Continuing with theabove-stated example, being the “normal” heartrate of patient 232 is50-55 beats per minute and the “normal” respiratory rate is 9-11 breathsper minute, information process 10 may adjust 310 the lower range of theheart rate to 48 beats per minute and may adjust 310 the lower range ofthe respiratory rate to 8 breaths per minute . . . thus eliminating theoutlier (e.g., outliers 230).

Conversely and if an issue does exist with patient 232, informationprocess may address 314 the issue. Accordingly and if patient 232 is inrespiratory distress, information process 10 may e.g., notify a doctor,make an emergency announcement, notify medical staff, etc.

Establishing Norms for a Device:

The following discussion concerns the manner in which informationprocess 10 may be utilized to establish norms for a specific patientover a defined period of time. For example and when onboarding a patientwithin a hospital, generalized norms (as discussed above) may beutilized. However and as is often the case, societal norms may not beapplicable to a specific individual. So while societal norms may beinitially utilized, they may prove to be inapplicable over time on anindividual basis.

Referring also to FIG. 5 and as discussed above, information process 10may monitor 400 a device (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206) to receive data signals(e.g., one or more of data signals 200 and/or one or more of datasignals 204) indicative of the device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206), whereinthe data signals (e.g., one or more of data signals 200 and/or one ormore of data signals 204) may concern one or more details of the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) and/or uses of the device (e.g., one or moreof first vendor devices 202 and/or one or more of second vendor devices206).

As discussed above, the device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206) may includeone or more of: a medical device, a process control device, a networkingdevice, a computing device, a manufacturing device, an agriculturaldevice, an energy/refining device, an aerospace device, a forestrydevice, and a defense device. Further and as discussed above, the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may include one or more sub devices (e.g.,sub devices 224, 226).

Information process 10 may process 402 the data signals (e.g., one ormore of data signals 200 and/or one or more of data signals 204) over adefined period of time to automatically define one or more definedsignal norms for the data signals (e.g., one or more of data signals 200and/or one or more of data signals 204). As discussed above, whenonboarding a patient (e.g., patient 232) within a hospital, generalizednorms (e.g., societal norms) may be utilized. However and as alsodiscussed above, these generalized norms (e.g., societal norms) areoften inapplicable with respect specific patients. Accordingly,information process 10 may process 402 these data signals (e.g., one ormore of data signals 200 and/or one or more of data signals 204) over adefined period of time (e.g., several minutes, several hours, severaldays, etc.) so that information process 10 may automatically define oneor more defined signal norms (e.g., defined signal norms 228) for thedata signals (e.g., one or more of data signals 200 and/or one or moreof data signals 204)

When processing 402 the data signals (e.g., one or more of data signals200 and/or one or more of data signals 204) over a defined period oftime (e.g., several minutes, several hours, several days, etc.) toautomatically define one or more defined signal norms (e.g., definedsignal norms 228) for the data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204), information process10 may: examine 404 a range of the data signals (e.g., one or more ofdata signals 200 and/or one or more of data signals 204).

Continuing with the above stated example, assume that the defined signalnorms (e.g., defined signal norms 228) for a heart rate is 60-100 beatsper minute and a respiratory rate is 12-20 breaths per minute.Accordingly and when onboarding patient 232 into the hospital, such“societal” norms may be used. However, information process may process402 the data signals (e.g., one or more of data signals 200 and/or oneor more of data signals 204) over a defined period of time (e.g.,several minutes, several hours, several days, etc.) so that informationprocess 10 may automatically define one or more defined signal norms(e.g., defined signal norms 228) for the data signals (e.g., one or moreof data signals 200 and/or one or more of data signals 204).

As discussed above, if the patient (e.g., patient 232) is a seasonedathlete of exceptional health, their “normal” heartrate may be 50-55beats per minute and their “normal” respiratory rate may be 9-11 breathsper minute. Accordingly, the use of “societal” norms with respect to thedata signals (e.g., one or more of data signals 200 and/or one or moreof data signals 204) may result in an abundance of “false” alarms beingissued by the device (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206) due to the appearancethat patient 232 has a very low heart rate of 50-55 beats per minute(when the “societal” norm is 60-100) and a very low respiratory rate of9-11 breaths per minute (when the societal norm is 12-20). Accordingly,information process 10 may examine 404 a range of the data signals(e.g., one or more of data signals 200 and/or one or more of datasignals 204) and identify that patient 232 has the following data signalranges: heart rate of 50-55 beats per minute (even though the “societal”norm is 60-100); and a respiratory rate of 9-11 breaths per minute (eventhough the societal norm is 12-20).

Further and when processing 402 the data signals (e.g., one or more ofdata signals 200 and/or one or more of data signals 204) over a definedperiod of time (e.g., several minutes, several hours, several days,etc.) to automatically define one or more defined signal norms (e.g.,defined signal norms 228) for the data signals (e.g., one or more ofdata signals 200 and/or one or more of data signals 204), informationprocess may calculate 406 one or more standard deviations of the datasignals (e.g., one or more of data signals 200 and/or one or more ofdata signals 204).

Standard deviation is a statistical measure that quantifies the amountof variation or dispersion in a dataset. It provides a numerical valuethat indicates how spread out the data points are from the mean(average) of the dataset. When considering a data range, the standarddeviation can provide insights into the variability within that range.It helps assess the extent to which data points deviate from the meanvalue within the given range.

To calculate the standard deviation within a data range, you wouldtypically follow these steps:

-   -   Calculate the mean (average) of the data within the range.    -   Subtract the mean from each data point within the range.    -   Square each of the differences obtained in step 2.    -   Calculate the average (mean) of the squared differences.    -   Take the square root of the average obtained in step 4.

The resulting value is the standard deviation within the specified datarange. It represents the average amount by which data points deviatefrom the mean within that particular range. A larger standard deviationindicates greater variability or dispersion, meaning the data pointswithin the range are more spread out from the mean. Conversely, asmaller standard deviation suggests less variation and a tighterclustering of data points around the mean within the range.

Additionally and when processing 402 the data signals (e.g., one or moreof data signals 200 and/or one or more of data signals 204) over adefined period of time (e.g., several minutes, several hours, severaldays, etc.) to automatically define one or more defined signal norms(e.g., defined signal norms 228) for the data signals (e.g., one or moreof data signals 200 and/or one or more of data signals 204), informationprocess may: iteratively redefine 408 the one or more defined signalnorms (e.g., defined signal norms 228) based upon updated data signals(e.g., one or more of data signals 200 and/or one or more of datasignals 204) received from the device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206). Forexample, information process 10 may iteratively redefine 408 (e.g.,every 10 seconds, or every one minute, or every few minutes, etc.) thedefined signal norms (e.g., defined signal norms 228) based upon updateddata signals (e.g., one or more of data signals 200 and/or one or moreof data signals 204) received from the device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206). In the configuration, the compute requirements of informationprocess 10 may be reduced at the expense of reduced performance.

Further and when processing 402 the data signals (e.g., one or more ofdata signals 200 and/or one or more of data signals 204) over a definedperiod of time (e.g., several minutes, several hours, several days,etc.) to automatically define one or more defined signal norms (e.g.,defined signal norms 228) for the data signals (e.g., one or more ofdata signals 200 and/or one or more of data signals 204), informationprocess may: continuously redefine 410 the one or more defined signalnorms based upon updated data signals (e.g., one or more of data signals200 and/or one or more of data signals 204) received from the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206). For example, information process 10 maycontinuously redefine 410 (e.g., every few milliseconds, every time newdata is received, etc.) the defined signal norms (e.g., defined signalnorms 228) based upon updated data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204) received from thedevice (e.g., one or more of first vendor devices 202 and/or one or moreof second vendor devices 206). In the configuration, the performance ofinformation process 10 may be increased at the expense of increasedcompute requirements.

Information process 10 may monitor 412 the device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206) to receive subsequent data signals (e.g., data signals 234)indicative of the device (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206), wherein informationprocess 10 may compare 414 the subsequent data signals (e.g., datasignals 234) to the defined signal norms (e.g., defined signal norms228) to identify outliers (e.g., outliers 230).

As discussed above, information process 10 may investigate 416 theoutliers (e.g., outliers 230) to determine if an issue exists with thedevice (e.g., one or more of first vendor devices 202 and/or one or moreof second vendor devices 206), wherein investigating 416 the outliers(e.g., outliers 230) to determine if an issue exists with the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may include physically investigating 418 theoutliers (e.g., outliers 230); and/or examining 420 other data signalsfrom the device (e.g., one or more of first vendor devices 202 and/orone or more of second vendor devices 206).

As discussed above, information process 10 may adjust 422 outlierdefinition criteria to eliminate the outlier (e.g., outliers 230) if anissue does not exist. For example and when adjusting 422 the outlierdefinition criteria, information process may define 424 bespoke outlierdefinition criteria for the device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206). Converselyand if an issue does exist with patient 232, information process 10 mayaddress by e.g., notifying a doctor, making an emergency announcement,notifying medical staff, etc.

Centralized Threshold Adjustment:

The following discussion concerns the manner in which informationprocess 10 may enable the centralized management of the thresholds,wherein these thresholds may be used by devices (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206) to establish norms for the patient being monitored. As discussedabove, oftentimes generalized norms are not applicable to specificpatients. And being these norms/thresholds are used to generate alarms,inapplicable norms/thresholds many result in an abundance of “false”alarms being issued by the device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206).Accordingly, information process 10 may enable the centralizedmanagement of such thresholds.

Referring also to FIG. 6 , information process 10 may interface 500 witha plurality of bedside monitoring devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) toreceive data signals (e.g., one or more of data signals 200 and/or oneor more of data signals 204). These data signals (e.g., one or more ofdata signals 200 and/or one or more of data signals 204) may havemonitoring criteria, wherein the monitoring criteria may include one ormore thresholds.

As discussed above, examples of such monitoring criteria/thresholds mayinclude defined signal norms (e.g., defined signal norms 228). Thesedefined signal norms (e.g., defined signal norms 228) may includeuser-defined signal norms and/or machine-defined signal norms. Forexample and with respect to user-defined signal norms, such user-definedsignal norms may be the result of (in this example) medical studies,medical books, insurance charts, medical records, etc. Further and withrespect to machine-defined signal norms, such machine-defined signalnorms may be defined via massive data sets that are processed by machinelearning. Accordingly, such monitoring criteria (e.g., defined signalnorms 228), may include user-defined monitoring criteria and/ormachine-defined monitoring criteria.

As also discussed above, such monitoring criteria (e.g., defined signalnorms 228) may be compartmentalized by e.g., gender, race, age,location, device type, device class, seasonality, time of day, etc.Specifically, medical statistics may vary depending upon various factors(including gender, race, age, location, device type, device class,seasonality, and time of day), wherein these factors can influencehealth outcomes, disease prevalence, treatment response, and othermedical parameters.

As also discussed above, such data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204) may concern one ormore details of the plurality of bedside monitoring devices (e.g., oneor more of first vendor devices 202 and/or one or more of second vendordevices 206) and/or uses of the plurality of bedside monitoring devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206).

-   -   Device Details: One or more details of the device (e.g., one or        more of first vendor devices 202 and/or one or more of second        vendor devices 206) may concern one or more readings, signals        and/or alarms that are provided by the device and concern (in        the example) the vital signs of a patient.    -   Device Uses: One or more uses of the device (e.g., one or more        of first vendor devices 202 and/or one or more of second vendor        devices 206) may concern the manner in which the device is being        used (e.g., what is the device doing, what is the device being        used for, who is the device assigned/connected to, etc.).

As also discussed above, the plurality of bedside monitoring devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may span a plurality of vendors, wherein (asdiscussed above) information process may enable such multiple-vendordevices to communicate.

Information process 10 may enable 502 adjustment of one or more of themonitoring criteria (e.g., defined signal norms 228). For example,information process may enable 502 adjustment of one or more of themonitoring criteria (e.g., defined signal norms 228) by a user (e.g.,user 236) via a computing device (e.g., computing device 238). Examplesof user 236 may include but are not limited to a medical professional,such as a nurse, nurse supervisor, medical technician, physician'sassistant, physician, etc. Examples of the computing device (e.g.,computing device 238) may include but are not limited to a nurse'sworkstation, a tablet computer, a laptop computer, a desktop computer, asmart phone, etc.

As discussed above, the defined signal norms (e.g., defined signal norms228) for a heart rate may be 60-100 beats per minute and for arespiratory rate may be 12-breaths per minute. Accordingly, informationprocess 10 may enable 502 adjustment of one or more of the monitoringcriteria (e.g., namely defined signal norms of 60-100 beats per minutefor a heart rate and 12-20 breaths per minute for a respiratory rate) bythe user (e.g., user 236) via a computing device (e.g., computing device238). Additionally/alternatively, information process 10 may enable 502adjustment of one or more of the monitoring criteria (e.g., namelydefined signal norms of 60-100 beats per minute for a heart rate and12-20 breaths per minute for a respiratory rate) by the user (e.g., user236) by providing the user (e.g., user 236) with instructions (e.g.,graphical and/or text-based) concerning how to manually adjust the oneor more of the monitoring criteria (e.g., namely defined signal norms of60-100 beats per minute for a heart rate and 12-20 breaths per minutefor a respiratory rate) via e.g., a user interface (not shown) includedwithin the plurality of bedside monitoring devices (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206).

When enabling 502 the adjustment of one or more of these monitoringcriteria (e.g., defined signal norms 228), information process 10 may:enable 504 the remote adjustment of the one or more monitoring criteria(e.g., defined signal norms 228) on a single bedside monitoring device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206).

As discussed above, “societal” defined signal norms (e.g., definedsignal norms 228) may not work for everyone. So while the defined signalnorms (e.g., defined signal norms 228) for a heart rate may be 60-100beats per minute and a respiratory rate may be 12-20 breaths per minute;if a patient (e.g., patient 232) is a seasoned athlete of exceptionalhealth, their “normal” heartrate may be 50-55 beats per minute and their“normal” respiratory rate may be 9-11 breaths per minute. Accordingly,information process 10 may: enable 504 the remote adjustment of the oneor more monitoring criteria (e.g., defined signal norms 228) on a singlebedside monitoring device (e.g., the single bedside device associatedwith patient 232) so that the monitoring criteria for the heart rate ofpatient 232 may be adjusted downward from 60-100 beats per minute to50-55 beats per minute and the monitoring criteria for the respiratoryrate may be adjusted downward from 12-20 breaths per minute to 9-11breaths per minute, wherein such adjustment may be made by the user(e.g., user 236) via the computing device (e.g., computing device 238).

When enabling 502 the adjustment of one or more of these monitoringcriteria (e.g., defined signal norms 228), information process 10 may:enable 506 the remote adjustment of the one or more monitoring criteria(e.g., defined signal norms 228) on a plurality of bedside monitoringdevices (e.g., one or more of first vendor devices 202 and/or one ormore of second vendor devices 206).

For example, assume that the “societal” defined signal norms (e.g.,defined signal norms 228) are not working for the majority of peoplewithin e.g., a hospital, a unit, a ward, a clinic, etc. For example,assume that a large portion of the people within the hospital, the unit,the ward, the clinic, etc. have a heart rate that is slightly over 100(e.g., 101-105 beats per minute), resulting in the generation of aconsiderable number of false alarms. Accordingly, information process 10may enable 506 the remote adjustment of the one or more monitoringcriteria (e.g., defined signal norms 228) on a plurality of bedsidemonitoring devices (e.g., some or all of the devices within thehospital, the unit, the ward, the clinic, etc.) so that the monitoringcriteria for the heart rate of patients within the hospital, the unit,the ward, the clinic, etc. may be adjusted upward from 60-100 beats perminute to 60-110 beats per minute, wherein such adjustment may be madeby the user (e.g., user 236) via the computing device (e.g., computingdevice 238).

When enabling 502 the adjustment of one or more of these monitoringcriteria (e.g., defined signal norms 228), information process 10 may:enable 508 the remote adjustment of the one or more monitoring criteria(e.g., defined signal norms 228) on a plurality of bedside monitoringdevices (e.g., one or more of first vendor devices 202 and/or one ormore of second vendor devices 206) based upon device vendor and/ordevice type.

For example, assume that the default defined signal norms (e.g., definedsignal norms 228) concerning heart rate are 60-100 beats per minute (fordevices made by Company A), while the default defined signal norms(e.g., defined signal norms 228) concerning heart rate are 70-90 beatsper minute (for devices made by Company B). Assume that the defaultdefined signal norms (e.g., defined signal norms 228) for Company A(i.e., a heart rate are 60-100 beats per minute) appear to be workingproperly, as it is not triggering a high level of false alarms. However,the default defined signal norms (e.g., defined signal norms 228) forCompany B (i.e., a heart rate are 70-90 beats per minute) appear to notbe working properly, as it is triggering a high level of false alarms.Accordingly, information process 10 may enable 508 the remote adjustmentof the one or more monitoring criteria (e.g., defined signal norms 228)on a plurality of bedside monitoring devices (e.g., the bedside devicesmanufactured by Company B) so that the heart rate monitoring criteriafor the bedside devices manufactured by Company B may be adjusted from70-90 beats per minute to 60-100 beats per minute, wherein suchadjustment may be made by the user (e.g., user 236) via the computingdevice (e.g., computing device 238).

Information process 10 may monitor 510 the device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206) to receive subsequent data signals (e.g., data signals 234)indicative of the device (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206), wherein informationprocess 10 may compare 512 the subsequent data signals (e.g., datasignals 234) to the defined signal norms (e.g., defined signal norms228) to identify outliers (e.g., outliers 230).

As discussed above, information process 10 may investigate 514 theoutliers (e.g., outliers 230) to determine if an issue exists with thedevice (e.g., one or more of first vendor devices 202 and/or one or moreof second vendor devices 206), wherein investigating 514 the outliers(e.g., outliers 230) to determine if an issue exists with the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may include physically investigating theoutliers (e.g., outliers 230); and/or examining other data signals fromthe device (e.g., one or more of first vendor devices 202 and/or one ormore of second vendor devices 206).

As discussed above, information process 10 may adjust 516 outlierdefinition criteria to eliminate the outlier (e.g., outliers 230) if anissue does not exist. For example and when adjusting 516 the outlierdefinition criteria, information process may define 518 bespoke outlierdefinition criteria for the device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206). Converselyand if an issue does exist with patient 232, information process 10 mayaddress by e.g., notifying a doctor, making an emergency announcement,notifying medical staff, etc.

Automated Device Personalization:

The following discussion concerns the manner in which informationprocess 10 may enable the customization of a bedside monitoring device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) based upon patient information obtained froma medical record (e.g., an EMR and/or an EHR). As discussed above,oftentimes generalized norms are not applicable to specific patients.And being these norms are used to generate alarms, inapplicable normsmany result in an abundance of “false” alarms being issued by the device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206). Accordingly, information process 10 mayenable the setting of such norms based upon patient information tomitigate such false alarms.

Referring also to FIG. 7 , information process 10 may interface 600 witha bedside monitoring device (e.g., one or more of first vendor devices202 and/or one or more of second vendor devices 206) to receive datasignals (e.g., one or more of data signals 200 and/or one or more ofdata signals 204).

As also discussed above, such data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204) may concern one ormore details of the bedside monitoring device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206) and/or uses of the bedside monitoring devices (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206).

-   -   Device Details: One or more details of the device (e.g., one or        more of first vendor devices 202 and/or one or more of second        vendor devices 206) may concern one or more readings, signals        and/or alarms that are provided by the device and concern (in        the example) the vital signs of a patient.    -   Device Uses: One or more uses of the device (e.g., one or more        of first vendor devices 202 and/or one or more of second vendor        devices 206) may concern the manner in which the device is being        used (e.g., what is the device doing, what is the device being        used for, who is the device assigned/connected to, etc.).

Further and as discussed above, the bedside monitoring device (e.g., oneor more of first vendor devices 202 and/or one or more of second vendordevices 206) may include one or more bedside monitoring sub devices(e.g., sub devices 224, 226).

Information process 10 may automatically obtain 602 patient information(e.g., patient information 240) from a medical record (e.g., patientrecord 242) associated with a patient (e.g., patient 232) assigned tothe bedside monitoring device (e.g., one or more of first vendor devices202 and/or one or more of second vendor devices 206).

The patient information (e.g., patient information 240) may include butis not limited to one or more of: a patient name, a patient demographic(e.g., age, gender, income level, race, employment, location,homeownership, and level of education), a medical history of thepatient, a medication history of the patient, caregiver assignmenthistory (e.g., what medical professionals are assigned to the patient)and patient assignment history (e.g., what room is assigned to thepatient).

Examples of the medical record (e.g., patient record 242) may includebut are not limited to one or more of an EMR and an EHR.

EHR stands for Electronic Health Record. An EHR is a digital version ofa patient's paper medical records, containing comprehensive andorganized information about an individual's health and medical history.It is designed to be accessible, updated, and shared securely amongauthorized healthcare providers and organizations.

Key features of an EHR include:

-   -   Digital Health Information: EHRs contain a wide range of        health-related information, including patient demographics,        medical history, diagnoses, medications, allergies, laboratory        results, imaging reports, immunization records, and more. These        records are stored electronically, making them easily accessible        and searchable.    -   Comprehensive View: EHRs provide a holistic and longitudinal        view of a patient's health. They capture information from        various healthcare providers and settings, enabling authorized        users to access and review a patient's complete medical history,        facilitating better care coordination and continuity.    -   Data Entry and Updates: EHRs allow healthcare providers to enter        and update patient information electronically. This includes        clinical notes, examination findings, treatment plans, progress        notes, and other relevant data. EHR systems often include        templates and forms to assist in efficient data entry.    -   Interoperability: EHRs support the exchange and sharing of        health information across different healthcare settings and        systems. Interoperability enables seamless communication and        collaboration among healthcare providers, facilitating        coordinated care, referrals, and transitions between different        care settings.    -   Decision Support: EHRs often include decision support tools,        such as clinical guidelines, alerts, reminders, and drug        interaction checks. These features assist healthcare providers        in making informed decisions, improving patient safety, and        adhering to evidence-based practices.    -   Privacy and Security: EHRs prioritize the security and privacy        of patient information. They employ stringent safeguards to        protect against unauthorized access, data breaches, and ensure        compliance with relevant privacy regulations, such as the Health        Insurance Portability and Accountability Act (HIPAA) in the        United States.

The adoption of EHRs aims to enhance patient care, improve efficiency,reduce medical errors, support evidence-based practices, and facilitatehealth information exchange. It allows healthcare providers to accessaccurate and up-to-date patient information at the point of care,leading to better-informed decisions and improved patient outcomes.

EMR stands for Electronic Medical Record. An EMR is a digital version ofa patient's medical records that is maintained within a healthcareprovider's own system or network. It is similar to an Electronic HealthRecord (EHR), but with a narrower scope as it primarily focuses on themedical information specific to a single healthcare organization orpractice.

Here are some key aspects of an EMR:

-   -   Digital Storage: EMRs store patient health information        electronically within a specific healthcare organization's        database or network. They replace traditional paper-based        medical charts and records, making information more accessible        and easily retrievable.    -   Medical Information: EMRs primarily contain medical and clinical        information, including diagnoses, treatments, medications,        medical procedures, laboratory and imaging results, progress        notes, and other relevant data specific to the healthcare        provider's practice.    -   Organization-Specific: Unlike EHRs, which are designed to be        interoperable and shared across different healthcare settings,        EMRs are typically limited to a specific healthcare organization        or practice. They are customized to fit the workflows and        requirements of the particular healthcare provider using them.    -   Data Entry and Updates: Healthcare providers enter patient        information directly into the EMR system using electronic forms,        templates, or structured data entry. EMRs support efficient data        entry and updates, including capturing patient demographics,        medical history, examination findings, and treatment plans.    -   Clinical Decision Support: EMRs often include clinical decision        support features, such as drug interaction checks, alerts for        potential contraindications or allergies, reminders for        preventive care, and clinical guidelines. These tools assist        healthcare providers in making informed decisions and improving        patient care.    -   Privacy and Security: EMRs prioritize the privacy and security        of patient information, implementing measures to protect against        unauthorized access, data breaches, and compliance with relevant        privacy regulations, such as HIPAA in the United States.

EMRs are primarily used within a single healthcare organization orpractice to manage patient records, streamline clinical workflows, andsupport patient care. While they may not have the same level ofinteroperability as EHRs, efforts are being made to enhance dataexchange and integration between different systems to promote bettercare coordination and continuity across healthcare settings.

Accordingly, assume that patient 232 (i.e., John Smith) is admitted tothe hospital and is in Bed A in Room 203. Accordingly and once admitted,information process 10 may automatically obtain 602 patient information240 from patient record 242 associated with patient 232 (i.e., JohnSmith) assigned to the bedside monitoring device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206). As discussed above, this patient information (e.g., patientinformation 240) may include but is not limited to one or more of: apatient name, a patient demographic (e.g., age, gender, income level,race, employment, location, homeownership, and level of education), amedical history of the patient, a medication history of the patient,caregiver assignment history (e.g., what medical professionals areassigned to the patient) and patient assignment history (e.g., what roomis assigned to the patient).

Once obtained 602, information process 10 may provide 604 the patientinformation (e.g., patient information 240) to the bedside monitoringdevice (e.g., one or more of first vendor devices 202 and/or one or moreof second vendor devices 206). For example, information process 10 mayprovide 604 patient information 240 to the bedside monitoring device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) that identifies the name of patient 232, thenurse assigned to patient 232, the doctor assigned to patient 232, theadmission date of patient 232, the anticipated discharge date of patient232, the average blood pressure of patient 232, the average respiratoryrate of patient 232, the average blood gas level of patient 232, etc.

When providing 604 the patient information (e.g., patient information240) to the bedside monitoring device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206),information process 10 may: automatically provide 606 the patientinformation (e.g., patient information 240) to the bedside monitoringdevice (e.g., one or more of first vendor devices 202 and/or one or moreof second vendor devices 206). For example, information process 10 mayautomatically provide 606 the patient information (e.g., patientinformation 240) directly to the bedside monitoring device (e.g., one ormore of first vendor devices 202 and/or one or more of second vendordevices 206) in an automated fashion without the need for third partyassistance/intervention.

When providing 604 the patient information to the bedside monitoringdevice (e.g., one or more of first vendor devices 202 and/or one or moreof second vendor devices 206), information process 10 may: provide 608the patient information (e.g., patient information 240) to the bedsidemonitoring device (e.g., one or more of first vendor devices 202 and/orone or more of second vendor devices 206) via a third-party intermediary(e.g., third-party intermediary 244). For example, information process10 may: provide 608 the patient information (e.g., patient information240) indirectly to the bedside monitoring device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206), wherein the patient information 240 is first provided tothird-party intermediary 244 (e.g., a hospital administrator or medicaldevice professional) and third-party intermediary 244 subsequentlyprovides the patient information 240 to the bedside monitoring device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206).

Information process 10 may adjust 610 one or more monitoring criteriadefined within the bedside monitoring device (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206)based, at least in part, upon the patient information (e.g., patientinformation 240).

As discussed above, examples of such monitoring criteria may includedefined signal norms (e.g., defined signal norms 228) and/or one or moresignal thresholds. Assume that the default defined signal norms (e.g.,defined signal norms 228) concerning heart rate is 60-100 beats perminute. Further assume that the patient (e.g., patient 232) is aseasoned athlete of exceptional health and their “normal” heartrate isdefined within patient information 240 as 50-55 beats per minute.Accordingly, information process 10 may adjust 610 the monitoringcriteria for heart rate (as defined within the bedside monitoringdevice) downward from 60-100 beats per minute to e.g., beats per minutebased, at least in part, upon patient information 240.

Environment Baseline Setting:

The following discussion concerns the manner in which informationprocess 10 may help to battle alarm fatigue . . . the detrimental effectof false alarms within medical facilities. False alarms are asignificant concern within hospitals, as they can lead to alarm fatigue,decreased patient safety, and increased healthcare provider burden. Theprevalence of false alarms can vary depending upon multiple factors,including the specific hospital, the type of medical devices used, andthe clinical setting.

Studies have shown that the rate of false alarms in hospitals can bealarmingly high. For example, research conducted in intensive care units(ICUs) has reported false alarm rates ranging from 72% to 99%,indicating that the majority of alarms in these settings are falsepositives.

Several factors contribute to the occurrence of false alarms inhospitals:

-   -   Inadequate Alarm Parameters: Alarm systems may be set with        default or suboptimal alarm thresholds, leading to alarms that        trigger unnecessarily. This can be due to alarm settings being        too sensitive or not properly adjusted to patient-specific        conditions.    -   Device Malfunctions or Technical Issues: Faulty equipment or        technical issues with medical devices can result in false        alarms. For example, electrode or sensor detachment, poor signal        quality, or software glitches can generate false positive        alarms.    -   Lack of Contextual Information: Alarms may lack the necessary        contextual information to help healthcare providers accurately        interpret their significance. For instance, alarms may not        consider the patient's clinical condition, medications, or        concurrent therapies, leading to false alarms that do not        require immediate action.    -   Inefficient Alarm Management: Healthcare providers may be        overwhelmed by the sheer number of alarms, making it challenging        to respond promptly and appropriately. This can lead to alarm        fatigue, where healthcare providers become desensitized or        ignore alarms due to their frequency, potentially compromising        patient safety.

Addressing false alarms is a priority for healthcare organizations anddevice manufacturers. Efforts are being made to improve alarm managementsystems, enhance alarm customization options, implement better alarmalgorithms, and provide more contextual information to reduce falsepositives and improve the accuracy and relevance of alarms.Additionally, initiatives focusing on standardization, education, andguidelines are being developed to promote best practices in alarmmanagement and mitigate the impact of false alarms on patient care.Accordingly and as will be discussed below, information process 10 maybe configured to monitor a medical environment to determine theprevalence and severity of the alarm situation within the monitoredmedical environment.

Referring also to FIG. 8 , information process 10 may acousticallymonitor 700 a medical environment (e.g., hospital 246 . . . or a portionthereof) to generate an acoustic signal (e.g., acoustic signal 248)indicative of audio within the medical environment (e.g., hospital 246 .. . or a portion thereof).

When acoustically monitoring 700 a medical environment (e.g., hospital246 . . . or a portion thereof) to generate an acoustic signal (e.g.,acoustic signal 248) indicative of audio within the medical environment(e.g., hospital 246 . . . or a portion thereof), information process 10may acoustically monitor 702 a medical environment (e.g., hospital 246 .. . or a portion thereof) via an application (e.g., application 250)installed on a handheld electronic device (e.g., handheld electronicdevice 252), examples of which may include but are not limited to asmart phone, a tablet computer, a wireless dedicated device, etc.).

Additionally/alternatively and when acoustically monitoring 700 amedical environment (e.g., hospital 246 . . . or a portion thereof) togenerate an acoustic signal (e.g., acoustic signal 248) indicative ofaudio within the medical environment (e.g., hospital 246 . . . or aportion thereof), information process 10 may acoustically monitor 704 amedical environment (e.g., hospital 246 . . . or a portion thereof) viaa dedicated network device (e.g., dedicated network device 252), anexample of which may include but is not limited to a wall-mountedmicrophone.

Generally speaking, information process 10 acoustically monitors 700 themedical environment (e.g., hospital 246 . . . or a portion thereof) togenerate acoustic signal 248 indicative of audio within the medicalenvironment (e.g., hospital 246 . . . or a portion thereof) so that thequantity and quality of the alarms within the medical environment (e.g.,hospital 246 . . . or a portion thereof) may be detected and determined.Accordingly, information process 10 may process 706 the acoustic signal(e.g., acoustic signal 248) to identify one or more audible alarms(e.g., audible alarms 256, 258, 260, 262) within the medical environment(e.g., hospital 246 . . . or a portion thereof).

Information process 10 may categorize 708 the one or more audible alarms(e.g., audible alarms 256, 258, 260, 262), thus defining categorizedalarms (e.g., categorized alarms 264). For example and when categorizing708 the one or more audible alarms (e.g., audible alarms 256, 258, 260,262), information process 10 may: categorize 710 the one or more audiblealarms (e.g., audible alarms 256, 258, 260, 262) based upon one or moreof an alarm type, an alarm severity, an alarm duration, an alarmmagnitude, and an alarm frequency.

Specifically, the alarms generated by the bedside monitoring devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may vary in volume, frequency, pattern andduration based upon one or more of an alarm type, an alarm severity, analarm duration, an alarm magnitude, and an alarm frequency. Accordingly,information process 10 may be configured to categorize 710 audiblealarms 256, 258, 260, 262 based upon an alarm type, an alarm severity,an alarm duration, an alarm magnitude, and an alarm frequency

Information process 10 may process 712 the categorized alarms (e.g.,categorized alarms 264) and generate 714 a report (e.g., report 266)based, at least in part, upon the categorized alarms (e.g., categorizedalarms 264). For example, report 266 may identify the quantity andquality of audible alarms 256, 258, 260, 262 and present them withinreport 266 in accordance with one or more of alarm type, an alarmseverity, an alarm duration, an alarm magnitude, and an alarm frequency.

Further, information process 10 may set 716 a baseline for the medicalenvironment (e.g., hospital 246 . . . or a portion thereof) based, atleast in part, upon the categorized alarms (e.g., categorized alarms264). For example, the information defined within report 266 for themedical environment (e.g., hospital 246 . . . or a portion thereof) maybe compared to other medical environments to determine how the medicalenvironment (e.g., hospital 246 . . . or a portion thereof) compareswith these other medical environments so that such a baseline may beestablished. For example, report 266 may identify the medicalenvironment (e.g., hospital 246 . . . or a portion thereof) as e.g.,being:

-   -   XX % worse (or better) than the average medical environment with        respect to false alarms;    -   YY % worse (or better) than the average medical environment with        respect to critical alarms; and    -   ZZ % worse (or better) than the average medical environment with        respect to total alarms.

Accordingly and through the use of such a report (e.g., report 266), themedical environment (e.g., hospital 246 . . . or a portion thereof) maybe able to see what areas they are good in, as well as the areas inwhich they can improve (thus establishing a baseline). And by addressingthe areas that need improvement, staff retention may be improved bye.g., reducing alarm fatigue.

Information process 10 may train 718 an AI model (e.g., AI model 268)based, at least in part, upon the categorized alarms (e.g., categorizedalarms 264). For example, data set 270 may be generated and AI model 268may be trained based upon data set 270. Data set 270 may include e.g.,categorized alarms from the medical environment (e.g., hospital 246 . .. or a portion thereof) and from other medical environments (not shown),wherein AI model 268 may be trained by processing data set 270 toextract patterns hidden within data set 270.

Machine learning models extract patterns from a dataset through aprocess called training. During training, the model learns to recognizepatterns and relationships within the data by adjusting its internalparameters or weights. The general steps involved in pattern extractionby a machine learning model are as follows:

-   -   Data Preparation: The dataset is preprocessed and prepared to        ensure its quality and suitability for training. This may        involve tasks such as data cleaning, normalization, feature        selection, and splitting the dataset into training and testing        subsets.    -   Model Selection: The appropriate machine learning model is        selected based on the nature of the problem and the        characteristics of the dataset. Different types of models, such        as decision trees, neural networks, support vector machines, or        random forests, can be used depending on the problem and the        data.    -   Model Training: The selected model is trained using the training        dataset. During this phase, the model iteratively adjusts its        internal parameters based on the input data and the desired        output. It tries to find the optimal settings that minimize the        difference between the predicted output and the actual output in        the training data.    -   Pattern Extraction: As the model iteratively adjusts its        parameters, it learns to recognize patterns and relationships        present in the data. The model identifies features or        combinations of features that are most relevant for predicting        the target variable or making accurate classifications. These        patterns can be simple or complex and can involve various        features or variables within the dataset.    -   Evaluation and Validation: Once the model is trained, it is        evaluated using the testing dataset to assess its performance        and generalization ability. The model's ability to extract        patterns effectively is measured by evaluating its accuracy,        precision, recall, F1 score, or other appropriate metrics based        on the specific problem domain.    -   Application and Prediction: After training and validation, the        trained model can be used to make predictions or classify new,        unseen data based on the patterns it learned from the training        dataset. The model applies the extracted patterns to new input        data to generate predictions or classify instances based on the        trained relationships.

It's important to note that the success of pattern extraction depends onseveral factors, such as the quality and representativeness of thetraining data, the choice of appropriate features, the selection of anappropriate model, and the careful tuning of model parameters. Theprocess of extracting patterns from data is at the core of machinelearning, enabling models to learn from examples and make predictions orclassifications on new data.

Accordingly, by training 718 AI model 268 based, at least in part, uponcategorized alarms 264, various patterns may be extracted concerninge.g., average alarms counts/types and how they relate to patientdemographics, hospital locations, staffing levels, staff attritionlevels, staff satisfaction levels, etc.

Clustering Alarms to Define an Incident:

The following discussion concerns the manner in which informationprocess 10 may define the occurrence of a group of alarms as theoccurrence of an incident. Generally speaking, while the individualoccurrence of any of the group of alarms may not be a concern, theoccurrence of the entire group of alarms may be indicative of a biggerproblem (i.e., hence the occurrence of an incident).

Referring also to FIG. 9 , information process 10 may define 800 anincident (e.g., incident 272) as the occurrence of a plurality ofrequired alarms. For example, assume that the incident of heart failuremay be defined 800 as the occurrence of: low blood pressure, a rapidheart rate, and a low blood oxygen level. While the occurrence of any ofthese individual alarms may not be indicative of a more serious issue,when a person is experiencing all three of these issues (e.g., low bloodpressure, a rapid heart rate, and a low blood oxygen level), that personmay be experiencing heart failure. Accordingly, information process 10may define 800 incident 272 (e.g., heart failure) as the occurrence oflow blood pressure, a rapid heart rate, and a low blood oxygen level).

As discussed above, information process 10 may monitor 802 a pluralityof devices (e.g., one or more of first vendor devices 202 and/or one ormore of second vendor devices 206) to detect the occurrence of alarms.As discussed above, the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) mayinclude one or more of: a medical device, a process control device, anetworking device, a computing device, a manufacturing device, anagricultural device, an energy/refining device, an aerospace device, aforestry device, and a defense device. Further, the plurality of devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may be geographically proximate orgeographically dispersed. For example, the plurality of devices (e.g.,one or more of first vendor devices 202 and/or one or more of secondvendor devices 206) may be within one unit of a hospital, spread acrossan entire hospital, spread across a group of hospitals, spread across astate, or spread across a country.

For this example, assume that the bedside devices (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206) that are monitoring patient 232 generate three alarms (e.g., alarms274, 276, 278). Being information process 10 is monitoring 802 suchdevices (e.g., one or more of first vendor devices 202 and/or one ormore of second vendor devices 206), information process 10 will detectthe occurrence of the alarms, thus defining a plurality of detectedalarms (e.g., alarms 274, 276, 278).

For this example, assume that:

-   -   Detected alarm 274 indicates that patient 232 has low blood        pressure;    -   Detected alarm 276 indicates that patient 232 has a rapid heart        rate; and    -   Detected alarm 278 indicates that patient 232 has low oxygen        levels in their blood.

When monitoring 802 a plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) todetect the occurrence of alarms, information process 10 may: monitor 804the plurality of devices (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206) to receive data signals(e.g., data signals 200 and/or data signals 204) indicative of theplurality of devices (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206).

As also discussed above, such data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204) may concern one ormore details of the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206)and/or uses of the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206).

-   -   Device Details: One or more details of the device (e.g., one or        more of first vendor devices 202 and/or one or more of second        vendor devices 206) may concern one or more readings, signals        and/or alarms that are provided by the device and concern (in        the example) the vital signs of a patient.    -   Device Uses: One or more uses of the device (e.g., one or more        of first vendor devices 202 and/or one or more of second vendor        devices 206) may concern the manner in which the device is being        used (e.g., what is the device doing, what is the device being        used for, who is the device assigned/connected to, etc.).

When monitoring 802 a plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) todetect the occurrence of alarms, information process 10 may: compare 806the data signals (e.g., data signals 200 and/or data signals 204) todefined signal norms to identify one or more of the plurality ofdetected alarms (e.g., alarms 274, 276, 278).

As discussed above, examples of such defined signal norms (e.g., definedsignal norms 228) may include user-defined signal norms and/ormachine-defined signal norms. For example and with respect touser-defined signal norms, such user-defined signal norms may be theresult of (in this example) medical studies, medical books, insurancecharts, medical records, etc. Further and with respect tomachine-defined signal norms, such machine-defined signal norms may bedefined via massive data sets that are processed by machine learning.

As also discussed above, such defined signal norms (e.g., defined signalnorms 228) may be compartmentalized by e.g., gender, race, age,location, device type, device class, seasonality, time of day, etc.Specifically, medical statistics may vary depending upon various factors(including gender, race, age, location, device type, device class,seasonality, and time of day), wherein these factors can influencehealth outcomes, disease prevalence, treatment response, and othermedical parameters.

Information process 10 may define 808 the incident (e.g., incident 272)as having occurred if the plurality of detected alarms (e.g., alarms274, 276, 278) includes the plurality of required alarms (e.g., a lowblood pressure alarm, a rapid heart rate alarm, and a low blood oxygenlevel alarm).

As stated above and for this example:

-   -   Detected alarm 274 indicates that patient 232 has low blood        pressure;    -   Detected alarm 276 indicates that patient 232 has a rapid heart        rate; and    -   Detected alarm 278 indicates that patient 232 has low oxygen        levels in their blood.

Accordingly, information process 10 may define 808 incident 272 (e.g., aheart failure incident) as having occurred since detected alarm 274indicates that patient 232 has low blood pressure; detected alarm 276indicates that patient 232 has a rapid heart rate; and detected alarm278 indicates that patient 232 has low oxygen levels in their blood.

Oftentimes, the occurrence of a plurality of alarms is only significantif such alarms occurred in a temporarily-proximate fashion. For example,a low blood pressure alarm, followed by a rapid heart rate alarm a weeklater (when the low blood pressure alarm no longer exists), followed bya low blood oxygen level alarm a week later (when the low blood pressurealarm and the rapid heart rate alarm no longer exist) is probably NOTindicative of incident 272 (e.g., a heart failure incident). Accordinglyand when defining 800 an incident (e.g., a heart failure incident) asthe occurrence of a plurality of required alarms (e.g., a low bloodpressure alarm, a rapid heart rate alarm, and a low blood oxygen levelalarm), information process 10 may define 810 the incident (e.g., aheart failure incident) as the occurrence of a plurality of requiredalarms (e.g., a low blood pressure alarm, a rapid heart rate alarm, anda low blood oxygen level alarm) within a defined period of time.

Predicting an Incident:

As discussed above, information process 10 may define the occurrence ofa group of alarms as the occurrence of an incident. The followingdiscussion concerns the manner in which information process 10 maypredict the occurrence of an incident when a portion of the group ofalarms that defines such an incident has occurred.

Referring also to FIG. 10 and as discussed above, information process 10may define 900 an incident (e.g., incident 272) as the occurrence of aplurality of required alarms, wherein the incident of heart failure maybe defined 900 as the occurrence of: low blood pressure, a rapid heartrate, and a low blood oxygen level. As also discussed above, whendefining 900 an incident (e.g., a heart failure incident) as theoccurrence of a plurality of required alarms (e.g., a low blood pressurealarm, a rapid heart rate alarm, and a low blood oxygen level alarm),information process 10 may define 902 the incident (e.g., a heartfailure incident) as the occurrence of a plurality of required alarms(e.g., a low blood pressure alarm, a rapid heart rate alarm, and a lowblood oxygen level alarm) within a defined period of time.

Further and as discussed above, information process 10 may monitor 904 aplurality of devices (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206) to detect theoccurrence of alarms. As also discussed above, the plurality of devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may include one or more of: a medical device,a process control device, a networking device, a computing device, amanufacturing device, an agricultural device, an energy/refining device,an aerospace device, a forestry device, and a defense device. Furtherand as discussed above, the plurality of devices (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206) may be geographically proximate or geographically dispersed (e.g.,within one unit of a hospital, spread across an entire hospital, spreadacross a group of hospitals, spread across a state, or spread across acountry).

As discussed above, when monitoring 904 a plurality of devices (e.g.,one or more of first vendor devices 202 and/or one or more of secondvendor devices 206) to detect the occurrence of alarms, informationprocess 10 may: monitor 906 the plurality of devices (e.g., one or moreof first vendor devices 202 and/or one or more of second vendor devices206) to receive data signals (e.g., data signals 200 and/or data signals204) indicative of the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206).

As also discussed above, such data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204) may concern one ormore details of the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206)and/or uses of the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206).

As also discussed above, when monitoring 904 a plurality of devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) to detect the occurrence of alarms,information process 10 may: compare 908 the data signals (e.g., datasignals 200 and/or data signals 204) to defined signal norms to identifyone or more of the plurality of detected alarms (e.g., alarms 274, 276,278).

As discussed above, examples of such defined signal norms (e.g., definedsignal norms 228) may include user-defined signal norms and/ormachine-defined signal norms. For example and with respect touser-defined signal norms, such user-defined signal norms may be theresult of (in this example) medical studies, medical books, insurancecharts, medical records, etc. Further and with respect tomachine-defined signal norms, such machine-defined signal norms may bedefined via massive data sets that are processed by machine learning.

As also discussed above, such defined signal norms (e.g., defined signalnorms 228) may be compartmentalized by e.g., gender, race, age,location, device type, device class, seasonality, time of day, etc.Specifically, medical statistics may vary depending upon various factors(including gender, race, age, location, device type, device class,seasonality, and time of day), wherein these factors can influencehealth outcomes, disease prevalence, treatment response, and othermedical parameters.

Information process 10 may predict 910 the occurrence of the incident(e.g., incident 272) if a defined portion of the plurality of requiredalarms (e.g., a low blood pressure alarm, a rapid heart rate alarm, anda low blood oxygen level alarm) has occurred. For example, if a patientis experiencing e.g., low blood pressure and a rapid heart rate,information process 10 may predict 910 the occurrence of incident 272(e.g., heart failure), as a defined portion (e.g., ⅔^(rds)) of theplurality of required alarms (e.g., a low blood pressure alarm, a rapidheart rate alarm, and a low blood oxygen level alarm) have occurred (andinformation process 10 is anticipating that the patient will soon beexperiencing low blood oxygen levels).

For example and when predicting 910 the occurrence of the incident(e.g., incident 272) if a defined portion (e.g., ⅔^(rds)) of theplurality of required alarms (e.g., a low blood pressure alarm, a rapidheart rate alarm, and a low blood oxygen level alarm) has occurred,information process 10 may: require 912 that the defined portion (e.g.,⅔^(rds)) of the plurality of required alarms (e.g., a low blood pressurealarm, a rapid heart rate alarm, and a low blood oxygen level alarm)have occurred in a defined sequence.

As discussed above, low blood pressure, a rapid heart rate, and a lowblood oxygen level are the three events that define the occurrence ofheart failure (e.g., incident 272). However, the sequence in which theseevents occur may be important to making a prediction of heart failure.For example, history may show that low blood pressure and a rapid heartrate will likely result in a low blood oxygen level shortly thereafter;thus enabling information process 10 to predict 910 the occurrence ofincident 272 (e.g., heart failure), anticipating that the patient willsoon be experiencing low blood oxygen levels. However, history may showthat a low blood oxygen level and low blood pressure may not result in arapid heart rate shortly thereafter; thus preventing information process10 from predicting 910 the occurrence of incident 272 (e.g., heartfailure), anticipating that the patient will not soon be experiencing arapid heart rate.

Further and when predicting 910 the occurrence of the incident (e.g.,incident 272) if a defined portion (e.g., ⅔^(rds)) of the plurality ofrequired alarms (e.g., a low blood pressure alarm, a rapid heart ratealarm, and a low blood oxygen level alarm) has occurred, informationprocess 10 may: require 914 that the defined portion (e.g., ⅔^(rds)) ofthe plurality of required alarms (e.g., a low blood pressure alarm, arapid heart rate alarm, and a low blood oxygen level alarm) haveoccurred within a defined period of time. As discussed above, theoccurrence of a plurality of alarms is only significant if such alarmsoccurred in a temporarily-proximate fashion. For example, a low bloodpressure alarm, followed by a rapid heart rate alarm a week later (whenthe low blood pressure alarm no longer exists), followed by a low bloodoxygen level alarm a week later (when the low blood pressure alarm andthe rapid heart rate alarm no longer exist) is probably NOT indicativeof incident 272 (e.g., a heart failure incident). Accordingly and whenpredicting 910 the occurrence of the incident (e.g., incident 272) if adefined portion (e.g., ⅔^(rds)) of the plurality of required alarms(e.g., a low blood pressure alarm, a rapid heart rate alarm, and a lowblood oxygen level alarm) has occurred, information process 10 may:require 914 that the defined portion (e.g., ⅔^(rds)) of the plurality ofrequired alarms (e.g., a low blood pressure alarm, a rapid heart ratealarm, and a low blood oxygen level alarm) have occurred within adefined period of time.

The defined portion (e.g., ⅔^(rds)) of the plurality of required alarms(e.g., a low blood pressure alarm, a rapid heart rate alarm, and a lowblood oxygen level alarm) may be defined via massive data sets that areprocessed by machine learning. As discussed above, information process10 may train an AI model (e.g., AI model 268) based, at least in part,upon the categorized alarms (e.g., categorized alarms 264). For example,data set 270 may be generated and AI model 268 may be trained based upondata set 270. Data set 270 may include e.g., categorized alarms from themedical environment (e.g., hospital 246 . . . or a portion thereof) andfrom other medical environments (not shown), wherein AI model 268 may betrained by processing data set 270 to extract patterns hidden withindata set 270.

Machine learning models extract patterns from a dataset through aprocess called training. During training, the model learns to recognizepatterns and relationships within the data by adjusting its internalparameters or weights. Accordingly, by training AI model 268 based, atleast in part, upon categorized alarms 264, various patterns may beextracted concerning e.g., average alarms counts/types and how theyrelate to patient demographics, hospital locations, staffing levels,staff attrition levels, staff satisfaction levels, etc.

Clustering Incidents to Define an Event:

As discussed above, information process 10 may define the occurrence ofa group of alarms as the occurrence of an incident. Generally speaking,while the individual occurrence of any of the group of alarms may not bea concern, the occurrence of the entire group of alarms may beindicative of a bigger problem (i.e., hence the occurrence of anincident). Further and as will be discussed below, information processmay define the occurrence of a group of incidents as the occurrence ofan event. Generally speaking, while the individual occurrence of any ofthe group of incidents may not be a concern, the occurrence of theentire group of incidents may be indicative of a bigger problem (i.e.,hence the occurrence of an event).

Referring also to FIG. 11 and as discussed above, information process 10may define 1000 an incident (e.g., incident 272) as the occurrence of aplurality of required alarms, wherein the incident of heart failure maybe defined 1000 as the occurrence of: low blood pressure, a rapid heartrate, and a low blood oxygen level. As also discussed above, whendefining 1000 an incident (e.g., a heart failure incident) as theoccurrence of a plurality of required alarms (e.g., a low blood pressurealarm, a rapid heart rate alarm, and a low blood oxygen level alarm),information process 10 may define 1002 the incident (e.g., a heartfailure incident) as the occurrence of a plurality of required alarms(e.g., a low blood pressure alarm, a rapid heart rate alarm, and a lowblood oxygen level alarm) within a defined period of time.

Information process 10 may define 1004 an event (e.g., event 280) as theoccurrence of a plurality of required incidents. For example, theoccurrence of incidents 272, 282, 284 may be indicative of theoccurrence of an event (e.g., event 280). For example, incident 272 isdeemed to have occurred if three alarms (e.g., alarms 274, 276, 278)have occurred. And similarly, information process 10 may deem incidents282, 284 to have occurred if a plurality of alarms (not shown)associated with each of incidents 282, 284 have occurred. Assume forthis example that the plurality of alarms (not shown) associated withincident 282 concern the functioning of the kidneys and, therefore,incident 282 may indicate renal failure. Further assume for this examplethat the plurality of alarms (not shown) associated with incident 284concern the functioning of the respiratory system and, therefore,incident 284 may indicate respiratory failure. Accordingly, whenincidents 272, 282, 284 (namely heart failure, renal failure andrespiratory failure) have occurred, information process 10 may deem theoccurrence of such incidents to be indicative of the occurrence of event280 (namely systemic organ failure).

Further, while incidents 272, 282, 284 are described above as beingdifferent incidents that result in event 280, this is for illustrativepurposes only, as other configurations are possible and are consideredto be within the scope of this disclosure. For example, assume that eachof incidents 272, 282, 284 is the same (e.g., ricin poisoning).Accordingly, if three ricin incidents occur, event 280 may be aterrorist attack.

When defining 1004 an event (e.g., event 280) as the occurrence of aplurality of required incidents (e.g., incidents 272, 282, 284),information process 10 may: define 1006 an event (e.g., event 280) asthe occurrence of a plurality of required incidents (e.g., incidents272, 282, 284) within a defined period of time.

Oftentimes, the occurrence of a plurality of incidents is onlysignificant if such incidents occurred in a temporarily-proximatefashion. For example, a heart failure incident, followed by a renalfailure incident a week later (when the heart failure incident no longerexists), followed by a respiratory failure incident a week later (whenthe heart failure incident and the renal failure incident no longerexist) is probably NOT indicative of event 280 (e.g., a systemic organfailure event). Accordingly and when defining 1004 an event (e.g., event280) as the occurrence of a plurality of required incidents (e.g., aheart failure incident, a renal failure incident and a respiratoryfailure incident), information process 10 may: define 1006 the event(e.g., a systemic organ failure event) as the occurrence of a pluralityof required incidents (e.g., a heart failure incident, a renal failureincident and a respiratory failure incident) within a defined period oftime

As discussed above, information process 10 may monitor 1008 a pluralityof devices (e.g., one or more of first vendor devices 202 and/or one ormore of second vendor devices 206) to detect the occurrence of alarms.As discussed above, the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) mayinclude one or more of: a medical device, a process control device, anetworking device, a computing device, a manufacturing device, anagricultural device, an energy/refining device, an aerospace device, aforestry device, and a defense device. Further, the plurality of devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) may be geographically proximate orgeographically dispersed. For example, the plurality of devices (e.g.,one or more of first vendor devices 202 and/or one or more of secondvendor devices 206) may be within one unit of a hospital, spread acrossan entire hospital, spread across a group of hospitals, spread across astate, or spread across a country.

When monitoring 1008 a plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) todetect the occurrence of alarms, information process 10 may: monitor1010 the plurality of devices (e.g., one or more of first vendor devices202 and/or one or more of second vendor devices 206) to receive datasignals (e.g., data signals 200 and/or data signals 204) indicative ofthe plurality of devices (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206).

As also discussed above, such data signals (e.g., one or more of datasignals 200 and/or one or more of data signals 204) may concern one ormore details of the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206)and/or uses of the plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206).

When monitoring 1008 a plurality of devices (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) todetect the occurrence of alarms, information process 10 may: compare1012 the data signals (e.g., data signals 200 and/or data signals 204)to defined signal norms to identify one or more of the plurality ofdetected alarms (e.g., alarms 274, 276, 278).

As discussed above, examples of such defined signal norms (e.g., definedsignal norms 228) may include user-defined signal norms and/ormachine-defined signal norms. For example and with respect touser-defined signal norms, such user-defined signal norms may be theresult of (in this example) medical studies, medical books, insurancecharts, medical records, etc. Further and with respect tomachine-defined signal norms, such machine-defined signal norms may bedefined via massive data sets that are processed by machine learning.

As also discussed above, such defined signal norms (e.g., defined signalnorms 228) may be compartmentalized by e.g., gender, race, age,location, device type, device class, seasonality, time of day, etc.Specifically, medical statistics may vary depending upon various factors(including gender, race, age, location, device type, device class,seasonality, and time of day), wherein these factors can influencehealth outcomes, disease prevalence, treatment response, and othermedical parameters.

As discussed above, information process 10 may define 1004 an event(e.g., event 280) as the occurrence of a plurality of requiredincidents. Assume for this example that the plurality of alarms (notshown) associated with incident 282 concern the functioning of thekidneys and, therefore, incident 282 may indicate renal failure. Furtherassume for this example that the plurality of alarms (not shown)associated with incident 284 concern the functioning of the respiratorysystem and, therefore, incident 284 may indicate respiratory failure.Accordingly, when incidents 272, 282, 284 (namely heart failure, renalfailure and respiratory failure) have occurred, information process 10may deem the occurrence of such incidents to be indicative of theoccurrence of event 280 (namely systemic organ failure).

Accordingly, information process 10 may define 1014 the event (e.g.,event 280) as having occurred if the plurality of detected alarms (whichwere detected while monitoring 1008 the plurality of devices) includesthe plurality of required alarms for each of the plurality of requiredincidents (e.g., incidents 272, 282, 284). So if each of incidents 272,282, 284 requires a plurality of alarms to have occurred . . . and ifthe plurality of detected alarms includes the sum of the plurality ofrequired alarms associated with each of incidents 272, 282, 284,information process 10 may define 1014 the event (e.g., event 280) ashaving occurred.

Threshold Management:

The following discussion concerns the manner in which informationprocess 10 may help to battle alarm fatigue by processing detectedalarms to determine their authenticity and making the necessaryadjustments (e.g., to monitoring criteria) to reduce the quantity ofinauthentic alarms.

Referring also to FIG. 12 and as discussed above, information process 10may monitor 1100 a bedside monitoring device (e.g., one or more of firstvendor devices 202 and/or one or more of second vendor devices 206) todetect the occurrence of alarms (e.g., alarms 274, 276, 278). Asdiscussed above, the device (e.g., one or more of first vendor devices202 and/or one or more of second vendor devices 206) may include one ormore sub devices (e.g., sub devices 224, 226).

As discussed above, studies have shown that the rate of false alarms inhospitals can be alarmingly high. For example, research conducted inintensive care units (ICUs) has reported false alarm rates ranging from72% to 99%, indicating that the majority of alarms in these settings arefalse positives. Several factors contribute to the occurrence of falsealarms in hospitals:

-   -   Inadequate Alarm Parameters: Alarm systems may be set with        default or suboptimal alarm thresholds, leading to alarms that        trigger unnecessarily. This can be due to alarm settings being        too sensitive or not properly adjusted to patient-specific        conditions.    -   Device Malfunctions or Technical Issues: Faulty equipment or        technical issues with medical devices can result in false        alarms. For example, electrode or sensor detachment, poor signal        quality, or software glitches can generate false positive        alarms.    -   Lack of Contextual Information: Alarms may lack the necessary        contextual information to help healthcare providers accurately        interpret their significance. For instance, alarms may not        consider the patient's clinical condition, medications, or        concurrent therapies, leading to false alarms that do not        require immediate action.    -   Inefficient Alarm Management: Healthcare providers may be        overwhelmed by the sheer number of alarms, making it challenging        to respond promptly and appropriately. This can lead to alarm        fatigue, where healthcare providers become desensitized or        ignore alarms due to their frequency, potentially compromising        patient safety.

As alarms are detected during the above-described monitoring 1100operation, information process 10 may process 1102 the detected alarms(e.g., alarms 274, 276, 278) to determine their authenticity.

For example and when processing 1102 the detected alarms (e.g., alarms274, 276, 278) to determine their authenticity, information process 10may: define 1104 volume information for the detected alarms (e.g.,alarms 274, 276, 278); and/or utilize 1106 the volume information todetermine the authenticity of the detected alarms (e.g., alarms 274,276, 278).

-   -   Volume: This signal measure indicates the amount of vital sign        sample measurements with respect to the thresholds, e.g., the        percentage of samples across the last 30 minutes and 240 minutes        where vital measure level is within 90% of the threshold level.        When the volume metric is high, i.e., greater than 0.8 across a        30-minute look-back and 0.5 across a 240-minute look-back, it        means a high volume of measures in recent history as compared to        a longer span of recent history are near the threshold and the        condition is met for updating a threshold in the        non-conservative direction (i.e., increasing the upper or        decreasing the lower). When the volume metric is low, it means a        low sample count of measures in recent history and satisfies the        condition to update a threshold in the conservative direction        (i.e., decreasing the upper threshold or increasing the lower).

Additionally and when processing 1102 the detected alarms (e.g., alarms274, 276, 278) to determine their authenticity, information process 10may: define 1108 volatility information for the detected alarms (e.g.,alarms 274, 276, 278); and/or utilize 1110 the volatility information todetermine the authenticity of the detected alarms (e.g., alarms 274,276, 278).

-   -   Volatility: The next signal measure involves the level of        erratic or volatile behavior. By comparing the variance of the        signal over the most recent short history, e.g., last 30        minutes, to the variance of the signal over the most recent        history over a longer span of time, e.g., last 240 minutes, we        can deduce whether the signal is volatile or non-volatile. When        the variance over the last 30 minutes, for example, exceeds that        of the last 240 minutes then we can deduce the signal is too        volatile for threshold adjustment. Conversely, when the opposite        is true the signal is stable and this second condition is met        for threshold adjustment.

Further and when processing 1102 the detected alarms (e.g., alarms 274,276, 278) to determine their authenticity, information process 10 may:define 1112 bias information for the detected alarms (e.g., alarms 274,276, 278); and/or utilize 1114 the bias information to determine theauthenticity of the detected alarms (e.g., alarms 274, 276, 278).

-   -   Bias: This next measure involves understanding the shifting        behavior of the signal, or time-varying bias. By measuring the        instantaneous first derivative of the signal with respect to        time, aka the time rate of change of the signal, aka the signal        “velocity”, we can understand when the signal is shifting and        which direction. For example, when a high percentage of samples        across the most recent history, e.g., last 30 or 60 minutes,        with non-zero instantaneous velocity, we can deduce the signal        is likely to be biased. When not found to be biased, this third        condition is met for threshold adjustment.

Additionally and when processing 1102 the detected alarms (e.g., alarms274, 276, 278) to determine their authenticity, information process 10may: define 1116 persistence information for the detected alarms (e.g.,alarms 274, 276, 278); and/or utilize 1118 the persistence informationto determine the authenticity of the detected alarms (e.g., alarms 274,276, 278).

-   -   Persistence: To understand whether the signal is persistent or        non-persistent (i.e., shifting), we compute the integral (i.e.,        area under the curve) of the difference between the average        velocity (or slope) of the signal across the most recent 30        minutes and the average velocity (or slope) of the signal across        the most recent 240 minutes. When the 30-minute slope exceeds        the 240 minute slope (i.e., the integral is positive), then the        signal is said to be persistent (i.e., not shifting overall) and        appropriate for threshold update.

Further and when processing 1102 the detected alarms (e.g., alarms 274,276, 278) to determine their authenticity, information process 10 may:define 1120 stationary information for the detected alarms (e.g., alarms274, 276, 278); and/or utilize 1122 the stationary information todetermine the authenticity of the detected alarms (e.g., alarms 274,276, 278).

-   -   Stationary: Lastly, measuring whether the signal is unchanged        over a timespan is important for understanding whether        statistics are changing or not. The signal needs to show        stationarity across recent history, e.g., the Augmented        Dickey-Fuller test is satisfied across a high percentage of        samples in recent history.

While the above discussion concerns volume information, volatilityinformation, bias information, persistence information, and stationaryinformation, this is for illustrative purposes only and is not intendedto be a limitation, as other configurations are possible and areconsidered to be within the scope of this disclosure, examples of whichmay include but are not limited to: pulse pressure (systolic bloodpressure−diastolic), mean arterial pressure, shock index (HR/systolicblood pressure), and external interventions that are perturbations toeffect the condition and behavior of the device (e.g., one or more offirst vendor devices 202 and/or one or more of second vendor devices206), such as e.g. medications (the time since, the rate, the totalamount) are important to consider here when deciding whether anadjustment to the threshold can be safely performed. If a detectedalarms (e.g., one or more of alarms 274, 276, 278) is determined to benon-authentic (in any of the fashions discussed above), informationprocess 10 may adjust 1124 one or more monitoring criteria that wasinstrumental in producing the non-authentic detected alarms (e.g., oneor more of alarms 274, 276, 278).

As discussed above, examples of such monitoring criteria may includedefined signal norms (e.g., defined signal norms 228) and/or one or moresignal thresholds. Assume as discussed above that the defined signalnorms (e.g., defined signal norms 228) concerning heart rate is 60-100beats per minute. Accordingly, the defined signal norm for heart ratehas a lower threshold of 60 and an upper threshold of 100.

Accordingly and when adjusting 1124 one or more monitoring criteria thatwas instrumental in producing the non-authentic detected alarms (e.g.,one or more of alarms 274, 276, 278), information process 10 may: define1126 bespoke monitoring criteria for the bedside monitoring device(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206). As discussed above, while the defined signalnorms (e.g., defined signal norms 228) for a heart rate is 60-100 beatsper minute; if the patient (e.g., patient 232) is a seasoned athlete ofexceptional health, their “normal” heartrate may be 50-55 beats perminute. Accordingly and in such a situation, information process 10 may:define 1126 bespoke monitoring criteria (e.g., 50-55 beats per minute)for the bedside monitoring device (e.g., one or more of first vendordevices 202 and/or one or more of second vendor devices 206) that ismonitoring patient 232.

If a detected alarms (e.g., one or more of alarms 274, 276, 278) isdetermined to be authentic, information process 10 may effectuate 1128an appropriate medical response (e.g., notify a doctor, make anemergency announcement, notify medical staff, etc.). to the authenticdetected alarms (e.g., one or more of alarms 274, 276, 278).

Operations Health UX:

The following discussion concerns the manner in which informationprocess 10 may render an Operations Health user experience that enablesa user to visually monitor the operations within one or more medicalinstitutions.

UX stands for User Experience. It refers to the overall experience andsatisfaction that a user has when interacting with a product, system, orservice. UX encompasses various elements, including usability,accessibility, ease of use, efficiency, and overall user satisfaction.UX design involves understanding the users' needs, preferences, andgoals and designing the product or system in a way that optimizes theirexperience. It aims to create intuitive, user-friendly, and enjoyableinteractions that meet the users' expectations and enhance their overallsatisfaction.

Some key aspects of UX design include:

-   -   User Research: Gathering insights about the target users through        methods such as interviews, surveys, and observations to        understand their behaviors, needs, and pain points.    -   Information Architecture: Organizing and structuring the        information within a product or system to facilitate easy        navigation and findability. This involves designing menus,        categories, and hierarchies to ensure users can locate        information or perform tasks efficiently.    -   Interaction Design: Designing the interactive elements and user        interfaces of a product or system. This involves creating        intuitive interfaces, designing clear and meaningful feedback        for user actions, and considering the overall flow and sequence        of user interactions.    -   Visual Design: Enhancing the visual appeal of the product or        system by considering color schemes, typography, iconography,        and other visual elements. Visual design aims to create a        visually pleasing and cohesive user interface that supports the        overall user experience.    -   Usability Testing: Conducting user testing sessions to evaluate        the usability and effectiveness of a product or system.        Usability testing helps identify areas of improvement and        ensures that the design aligns with the users' expectations and        needs.

The goal of UX design is to create products or systems that areintuitive, efficient, and enjoyable for users to interact with. Itinvolves considering the users' needs, goals, and context of use toprovide meaningful and satisfying experiences. By prioritizing userexperience, organizations can enhance customer satisfaction, increaseengagement, and build long-term user loyalty.

Referring also to FIGS. 13A-13D, information process 10 may gather 1200information from a datasource (e.g., datasource 54, FIG. 1 ) concerningone or more medical professionals (e.g., user 236) within one or moremedical institutions (e.g., hospital 246 . . . or a portion thereof),thus defining gathered information (e.g., gathered information 56).

As discussed above, examples of such medical professionals (e.g., user236) may include but are not limited to any people (e.g., a nurse, nursesupervisor, medical technician, physician's assistant, physician, etc.)that work for and/or are employed by the one or more medicalinstitutions (e.g., hospital 246 . . . or a portion thereof).

Datasource 54 may include any device that is capable of storinginformation concerning the one or more medical professionals (e.g., user236) of the one or more medical institutions (e.g., hospital 246 . . .or a portion thereof), examples of which may include but are not limitedto an employment database, a spreadsheet, a storage device, etc.

Generally speaking, gathered information 56 may concern, at least inpart, the wellbeing of one or more medical staff (e.g., nurses, nursesupervisors, medical technicians, physician's assistants, physicians,etc.) of the one or more medical institutions (e.g., hospital 246 . . .or a portion thereof).

The wellbeing of one or more medical staff (e.g., nurses, nursesupervisors, medical technicians, physician's assistants, physicians,etc.) may concerns one or more of:

-   -   the attrition potential of the one or more medical staff (e.g.,        nurses, nurse supervisors, medical technicians, physician's        assistants, physicians, etc.), namely what is the likelihood of        a particular staff member leaving the one or more medical        institutions (e.g., hospital 246 . . . or a portion thereof);    -   the fatigue level of the one or more medical staff (e.g.,        nurses, nurse supervisors, medical technicians, physician's        assistants, physicians, etc.), namely how fatigued (generally)        or how alarm fatigued (specifically) is a particular staff        member of the one or more medical institutions (e.g., hospital        246 . . . or a portion thereof);    -   the patient-loading of the one or more medical staff (e.g.,        nurses, nurse supervisors, medical technicians, physician's        assistants, physicians, etc.), namely what is the level of        patient loading of a particular staff member of the one or more        medical institutions (e.g., hospital 246 . . . or a portion        thereof); and    -   the alarm-loading of the one or more medical staff (e.g.,        nurses, nurse supervisors, medical technicians, physician's        assistants, physicians, etc.), namely what is the level of alarm        loading of a particular staff member of the one or more medical        institutions (e.g., hospital 246 . . . or a portion thereof).

The wellbeing of the one or more medical staff (e.g., nurses, nursesupervisors, medical technicians, physician's assistants, physicians,etc.) may be based, at least in part, upon the quantity and/orauthenticity of the alarms (e.g., one or more of alarms 274, 276, 278)to which the one or more medical staff (e.g., nurses, nurse supervisors,medical technicians, physician's assistants, physicians, etc.) weresubjected. As discussed above, as alarms are detected during theabove-described monitoring operation, information process 10 may processthe detected alarms (e.g., alarms 274, 276, 278) to determine theirauthenticity, wherein such authenticity may be determined by examininge.g., volume information, volatility information, bias information,persistence information, and stationary information

Information process 10 may enable 1202 a user (e.g., user 236) to selecta viewing lens from a plurality of available viewing lenses (e.g.,plurality of lens 286) through which to display the gathered information(e.g., gathered information 56), thus defining a selected viewing lens.The plurality of available viewing lenses (e.g., plurality of lens 286)may include one or more of:

-   -   Macro Level Viewing Lens 288: a lens that displays a portion of        gathered information 56 that concerns the wellbeing of the        medical staff (e.g., a nurse, a nurse supervisor, a medical        technician, a physician's assistant, a physician, etc.) at any        facility within the one or more medical institutions (e.g.,        hospital 246 . . . or a portion thereof).    -   Facility Level Viewing Lens 290: a lens that displays a portion        of gathered information 56 that concerns the wellbeing of the        medical staff (e.g., a nurse, a nurse supervisor, a medical        technician, a physician's assistant, a physician, etc.) at a        particular facility within the one or more medical institutions        (e.g., hospital 246 . . . or a portion thereof), as illustrated        in FIG. 13B.    -   Unit Level Viewing Lens 292: a lens that displays a portion of        gathered information 56 that concerns the wellbeing of the        medical staff (e.g., a nurse, a nurse supervisor, a medical        technician, a physician's assistant, a physician, etc.) at a        particular unit of the one or more medical institutions (e.g.,        hospital 246 . . . or a portion thereof), as illustrated in FIG.        13C.    -   Cohort Level Viewing Lens 294: a lens that displays a portion of        gathered information 56 that concerns the wellbeing of a        selected group of medical staff (e.g., a nurse, a nurse        supervisor, a medical technician, a physician's assistant, a        physician, etc.) at the one or more medical institutions (e.g.,        hospital 246 . . . or a portion thereof).    -   Individual Level Viewing Lens 296: a lens that displays a        portion of gathered information 56 that concerns the wellbeing        of a particular medical staff (e.g., a nurse, a nurse        supervisor, a medical technician, a physician's assistant, a        physician, etc.) within the one or more medical institutions        (e.g., hospital 246 . . . or a portion thereof), as illustrated        in FIG. 13D.

Information process 10 may render 1204 at least a portion of thegathered information (e.g., gathered information 56) based, at least inpart, upon the selected viewing lens (e.g., chosen from macro levelviewing lens 288, facility level viewing lens 290, unit level viewinglens 292, cohort level viewing lens 294, individual level viewing lens296).

When rendering 1204 at least a portion of the gathered information(e.g., gathered information 56) based, at least in part, upon theselected viewing lens (e.g., chosen from macro level viewing lens 288,facility level viewing lens 290, unit level viewing lens 292, cohortlevel viewing lens 294, individual level viewing lens 296), informationprocess 10 may: graphically indicate 1206 information concerning thewellbeing of at least a portion of the one or more medical staff (e.g.,nurses, nurse supervisors, medical technicians, physician's assistants,physicians, etc.) of the one or more medical institutions (e.g.,hospital 246 . . . or a portion thereof).

When rendering 1204 at least a portion of the gathered information(e.g., gathered information 56) based, at least in part, upon theselected viewing lens, information process 10 may: provide 1208time-based information concerning the wellbeing of at least a portion ofthe one or more medical staff (e.g., nurses, nurse supervisors, medicaltechnicians, physician's assistants, physicians, etc.) of the one ormore medical institutions (e.g., hospital 246 . . . or a portionthereof).

Incident Patterns UX:

The following discussion concerns the manner in which informationprocess 10 may render an Incident Patterns user experience that enablesa user to visually monitor the operations within one or more medicalinstitutions.

Referring also to FIGS. 14A-14D, information process 10 may gather 1300information from a datasource (e.g., datasource 54, FIG. 1 ) concerningone or more incidents (e.g., incident 272) within one or more medicalinstitutions (e.g., hospital 246 . . . or a portion thereof), thusdefining gathered information (e.g., gathered information 56).

As discussed above, such incidents (e.g., incident 272) may be defined,at least in part, by one or more alarms (e.g., one or more of alarms274, 276, 278) occurring within the one or more medical institutions(e.g., hospital 246 . . . or a portion thereof).

As discussed above, one or more alarms (e.g., one or more of alarms 274,276, 278) may be originated, at least in part, on one or more devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) within the one or more medical institutions(e.g., hospital 246 . . . or a portion thereof).

As discussed above, the one or more alarms (e.g., one or more of alarms274, 276, 278) may be based, at least in part, upon one or morethresholds (e.g., a lower threshold of 60 and an upper threshold of 100for the defined signal norm for a heart rate) of the one or more devices(e.g., one or more of first vendor devices 202 and/or one or more ofsecond vendor devices 206) within the one or more medical institutions(e.g., hospital 246 . . . or a portion thereof).

Datasource 54 may include any device that is capable of storinginformation concerning such incidents (e.g., incident 272), examples ofwhich may include but are not limited to an incident database, aspreadsheet, a storage device, etc.

Generally speaking, gathered information 56 may concern, at least inpart, one or more incidents (e.g., incident 272) that occurred withinthe one or more medical institutions (e.g., hospital 246 . . . or aportion thereof).

Information process 10 may enable 1302 a user (e.g., user 236) to selecta viewing lens from a plurality of available viewing lenses (e.g.,plurality of lens 286) through which to display the gathered information(e.g., gathered information 56), thus defining a selected viewing lens.The plurality of available viewing lenses (e.g., plurality of lens 286)may include one or more of:

-   -   Macro Level Viewing Lens 288: a lens that displays a portion of        gathered information 56 that concerns the incidents at any        facility within the one or more medical institutions (e.g.,        hospital 246 . . . or a portion thereof).    -   Facility Level Viewing Lens 290: a lens that displays a portion        of gathered information 56 that concerns the incidents at a        particular facility within the one or more medical institutions        (e.g., hospital 246 . . . or a portion thereof), as illustrated        in FIG. 14B.    -   Unit Level Viewing Lens 292: a lens that displays a portion of        gathered information 56 that concerns the incidents at a        particular unit of the one or more medical institutions (e.g.,        hospital 246 . . . or a portion thereof), as illustrated in FIG.        14C.    -   Cohort Level Viewing Lens 294: a lens that displays a portion of        gathered information 56 that concerns the incidents of a        selected group of patients at the one or more medical        institutions (e.g., hospital 246 . . . or a portion thereof).    -   Individual Level Viewing Lens 296: a lens that displays a        portion of gathered information 56 that concerns the incidents        of a particular patient within the one or more medical        institutions (e.g., hospital 246 . . . or a portion thereof), as        illustrated in FIG. 14D.

Information process 10 may render 1304 at least a portion of thegathered information (e.g., gathered information 56) based, at least inpart, upon the selected viewing lens (e.g., chosen from macro levelviewing lens 288, facility level viewing lens 290, unit level viewinglens 292, cohort level viewing lens 294, individual level viewing lens296).

When rendering 1304 at least a portion of the gathered information(e.g., gathered information 56) based, at least in part, upon theselected viewing lens (e.g., chosen from macro level viewing lens 288,facility level viewing lens 290, unit level viewing lens 292, cohortlevel viewing lens 294, individual level viewing lens 296), informationprocess 10 may: graphically locate 1306 at least a portion of the one ormore incidents (e.g., incident 272) within at least a portion of the oneor more medical institutions (e.g., hospital 246 . . . or a portionthereof).

Information process 10 may enable 1308 a user (e.g., user 236) to adjustthe one or more thresholds (e.g., a lower threshold of 60 and an upperthreshold of 100 for the defined signal norm for a heart rate) of theone or more devices (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206) within the one or moremedical institutions (e.g., hospital 246 . . . or a portion thereof).

Threshold Manager UX:

The following discussion concerns the manner in which informationprocess 10 may render a Threshold Manager user experience that enables auser to visually monitor the operations within one or more medicalinstitutions.

Referring also to FIGS. 15A-15D, information process 10 may gather 1400information from a datasource (e.g., datasource 54, FIG. 1 ) concerningone or more thresholds (e.g., a lower threshold of 60 and an upperthreshold of 100 for the defined signal norm for a heart rate) withinone or more medical institutions (e.g., hospital 246 . . . or a portionthereof), thus defining gathered information (e.g., gathered information56).

As discussed above, such thresholds (e.g., a lower threshold of 60 andan upper threshold of 100 for the defined signal norm for a heart rate)may be defined, at least in part, within the one or more devices (e.g.,one or more of first vendor devices 202 and/or one or more of secondvendor devices 206) within the one or more medical institutions (e.g.,hospital 246 . . . or a portion thereof).

Datasource 54 may include any device that is capable of storinginformation concerning such thresholds (e.g., a lower threshold of 60and an upper threshold of 100 for the defined signal norm for a heartrate), examples of which may include but are not limited to an incidentdatabase, a spreadsheet, a storage device, etc.

Generally speaking, gathered information 56 may concern, at least inpart, one or more thresholds of one or more devices within the one ormore medical institutions.

Information process 10 may enable 1402 a user (e.g., user 236) to selecta viewing lens from a plurality of available viewing lenses (e.g.,plurality of lens 286) through which to display the gathered information(e.g., gathered information 56), thus defining a selected viewing lens.The plurality of available viewing lenses (e.g., plurality of lens 286)may include one or more of:

-   -   Macro Level Viewing Lens 288: a lens that displays a portion of        gathered information 56 that concerns the thresholds at any        facility within the one or more medical institutions (e.g.,        hospital 246 . . . or a portion thereof).    -   Facility Level Viewing Lens 290: a lens that displays a portion        of gathered information 56 that concerns the thresholds at a        particular facility within the one or more medical institutions        (e.g., hospital 246 . . . or a portion thereof).    -   Unit Level Viewing Lens 292: a lens that displays a portion of        gathered information 56 that concerns the thresholds at a        particular unit of the one or more medical institutions (e.g.,        hospital 246 . . . or a portion thereof), as illustrated in        FIGS. 15B-15C.    -   Cohort Level Viewing Lens 294: a lens that displays a portion of        gathered information 56 that concerns the thresholds of a        selected group of patients at the one or more medical        institutions (e.g., hospital 246 . . . or a portion thereof).    -   Individual Level Viewing Lens 296: a lens that displays a        portion of gathered information 56 that concerns the thresholds        of a particular patient within the one or more medical        institutions (e.g., hospital 246 . . . or a portion thereof), as        illustrated in FIG. 15D.

Information process 10 may render 1404 at least a portion of thegathered information (e.g., gathered information 56) based, at least inpart, upon the selected viewing lens (e.g., chosen from macro levelviewing lens 288, facility level viewing lens 290, unit level viewinglens 292, cohort level viewing lens 294, individual level viewing lens296).

When rendering 1404 at least a portion of the gathered information(e.g., gathered information 56) based, at least in part, upon theselected viewing lens (e.g., chosen from macro level viewing lens 288,facility level viewing lens 290, unit level viewing lens 292, cohortlevel viewing lens 294, individual level viewing lens 296), informationprocess 10 may: graphically locate 1406 at least a portion of the one ormore thresholds within at least a portion of the one or more medicalinstitutions (e.g., hospital 246 . . . or a portion thereof).

Information process 10 may enable 1408 a (e.g., user 236) to adjust theone or more thresholds (e.g., a lower threshold of 60 and an upperthreshold of 100 for the defined signal norm for a heart rate) of theone or more devices (e.g., one or more of first vendor devices 202and/or one or more of second vendor devices 206) within the one or moremedical institutions (e.g., hospital 246 . . . or a portion thereof).

Of particular interest is FIGS. 15B-15C, which show two representationsof the same information. Specifically, FIG. 15B shows the thresholds andthe level at which these thresholds are currently being exceeded (i.e.,bigger and/or darker indicators are indicative of more serious alarms)within the medical facility. FIG. 15C shows the same area within themedical facility but illustrates what the levels would be like ifinformation process 10 was utilized to adjust the thresholds to a levelthat would reduce false alarms.

Alarm Insights UX:

The following discussion concerns the manner in which informationprocess 10 may render an Alarm Insights user experience that enables auser to visually monitor the operations within one or more medicalinstitutions.

Referring also to FIGS. 16A-16B, information process 10 may gather 1500information from a datasource (e.g., datasource 54, FIG. 1 ) concerningone or more alarms (e.g., alarms 274, 276, 278) within one or moremedical institutions (e.g., hospital 246 . . . or a portion thereof),thus defining gathered information (e.g., gathered information 56).

As discussed above, such alarms (e.g., alarms 274, 276, 278) may bebased, at least in part, upon one or more thresholds (e.g., a lowerthreshold of 60 and an upper threshold of 100 for the defined signalnorm for a heart rate) and originated, at least in part, on the one ormore devices (e.g., one or more of first vendor devices 202 and/or oneor more of second vendor devices 206) within the one or more medicalinstitutions (e.g., hospital 246 . . . or a portion thereof).

Datasource 54 may include any device that is capable of storinginformation concerning such alarms (e.g., alarms 274, 276, 278),examples of which may include but are not limited to an incidentdatabase, a spreadsheet, a storage device, etc.

Generally speaking, gathered information 56 may concern, at least inpart, one or more alarms (e.g., one or more of alarms 274, 276, 278)that occurred within the one or more medical institutions (e.g.,hospital 246 . . . or a portion thereof).

Information process 10 may enable 1502 a user (e.g., user 236) to selecta viewing lens from a plurality of available viewing lenses (e.g.,plurality of lens 286) through which to display the gathered information(e.g., gathered information 56), thus defining a selected viewing lens.The plurality of available viewing lenses (e.g., plurality of lens 286)may include one or more of:

-   -   Macro Level Viewing Lens 288: a lens that displays a portion of        gathered information 56 that concerns the alarms at any facility        within the one or more medical institutions (e.g., hospital 246        . . . or a portion thereof).    -   Facility Level Viewing Lens 290: a lens that displays a portion        of gathered information 56 that concerns the alarms at a        particular facility within the one or more medical institutions        (e.g., hospital 246 . . . or a portion thereof), as illustrated        in FIG. 16B.    -   Unit Level Viewing Lens 292: a lens that displays a portion of        gathered information 56 that concerns the alarms at a particular        unit of the one or more medical institutions (e.g., hospital 246        . . . or a portion thereof).    -   Cohort Level Viewing Lens 294: a lens that displays a portion of        gathered information 56 that concerns a selected group of alarms        at the one or more medical institutions (e.g., hospital 246 . .        . or a portion thereof).    -   Individual Level Viewing Lens 296: a lens that displays a        portion of gathered information 56 that concerns a single alarm        within the one or more medical institutions (e.g., hospital 246        . . . or a portion thereof).

Information process 10 may render 1504 at least a portion of thegathered information (e.g., gathered information 56) based, at least inpart, upon the selected viewing lens (e.g., chosen from macro levelviewing lens 288, facility level viewing lens 290, unit level viewinglens 292, cohort level viewing lens 294, individual level viewing lens296).

When rendering 1504 at least a portion of the gathered information(e.g., gathered information 56) based, at least in part, upon theselected viewing lens (e.g., chosen from macro level viewing lens 288,facility level viewing lens 290, unit level viewing lens 292, cohortlevel viewing lens 294, individual level viewing lens 296), informationprocess 10 may: graphically indicate 1506 information concerning the oneor more alarms (e.g., one or more of alarms 274, 276, 278) within one ormore medical institutions (e.g., hospital 246 . . . or a portionthereof).

When rendering 1504 at least a portion of the gathered information(e.g., gathered information 56) based, at least in part, upon theselected viewing lens (e.g., chosen from macro level viewing lens 288,facility level viewing lens 290, unit level viewing lens 292, cohortlevel viewing lens 294, individual level viewing lens 296), informationprocess 10 may: provide 1508 information concerning the quantity ofauthentic alarms identified and inauthentic alarms avoided.

Multi-Lens UX:

The following discussion concerns the manner in which informationprocess 10 may allow a user to gather and view information is a UX fromany type of organization (e.g., a medical organization, a processcontrol organization, a networking organization, a computingorganization, a manufacturing organization, an agriculturalorganization, an energy/refining organization, an aerospaceorganization, a forestry organization, and a defense organization).

Accordingly and referring also to FIG. 17 , information process 10 maygather 1600 information from a datasource (e.g., datasource 54, FIG. 1 )concerning one or more organizations, thus defining gathered information(e.g., gathered information 56). Datasource 54 may include any devicethat is capable of storing gathered information 56, examples of whichmay include but are not limited to an incident database, a spreadsheet,a storage device, etc.

The information (e.g., gathered information 56) may concern, at least inpart, the wellbeing of one or more staff of the one or moreorganizations; one or more incidents that occurred within the one ormore organizations; one or more thresholds of one or more devices withinthe one or more organizations; and one or more alarms that occurredwithin the one or more organizations.

Generally speaking, gathered information 56 may concern, at least inpart, any information about the one or more organizations, examples ofwhich may include but are not limited to:

-   -   Corporate/Company Information: Information concerning the        corporate structure of the organization.    -   Employment Information: Information concerning the employment        practices and employment structure of the organization.    -   Employee/Staff Information: Information concerning the        employees/staff of the organization, such as number of        employees, types of employees, and benefits provided to        employees.    -   Shareholder Information: Information concerning the        shareholders, equity structure, and equity type of the        organization.    -   Owner Information: Information concerning the owners/majority        shareholders of the organization.    -   Event Information: Information concerning events within the        organization, such as turnover events, attrition events,        advertising campaigns, legal events, active lawsuits and        historical lawsuits.    -   Tax Information: Information concerning the tax structure, tax        status, tax filings of the organization.    -   Product/Service Information: Information concerning the products        and/or services offered by the organization.    -   Production Information: Information concerning the production        levels/production targets of the organization.    -   Sales Information: Information concerning the sales levels/sale        targets of the organization.    -   Historical Information: Information concerning the history of        the organization.    -   Location Information: Information concerning the domestic        locations and foreign locations of the organization.

Information process 10 may enable 1602 a user (e.g., user 236) to selecta viewing lens from a plurality of available viewing lenses (e.g.,plurality of lens 286) through which to display the gathered information(e.g., gathered information 56), thus defining a selected viewing lens.The plurality of available viewing lenses (e.g., plurality of lens 286)may include one or more of:

-   -   Macro Level Viewing Lens 288: a lens that displays a portion of        gathered information 56 that concerns information at any        facility within the one or more organizations.    -   Facility Level Viewing Lens 290: a lens that displays a portion        of gathered information 56 that concerns information at a        particular facility within the one or more organizations.    -   Unit Level Viewing Lens 292: a lens that displays a portion of        gathered information 56 that concerns information at a        particular portion (unit/subsidiary/entity) of the one or more        organizations).    -   Cohort Level Viewing Lens 294: a lens that displays a portion of        gathered information 56 that concerns information for selected        group/sub-portion at the one or more organizations.    -   Individual Level Viewing Lens 296: a lens that displays a        portion of gathered information 56 that concerns a small item of        information concerning a single employee, a single corporate        event, a single tax filing, a sales of a single product within a        single region, etc.

Information process 10 may render 1604 at least a portion of thegathered information (e.g., gathered information 56) based, at least inpart, upon the selected viewing lens (e.g., chosen from macro levelviewing lens 288, facility level viewing lens 290, unit level viewinglens 292, cohort level viewing lens 294, individual level viewing lens296).

GENERAL

As will be appreciated by one skilled in the art, the present disclosuremay be embodied as a method, a system, or a computer program product.Accordingly, the present disclosure may take the form of an entirelyhardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,the present disclosure may take the form of a computer program producton a computer-usable storage medium having computer-usable program codeembodied in the medium.

Any suitable computer usable or computer readable medium may beutilized. The computer-usable or computer-readable medium may be, forexample but not limited to, an electronic, magnetic, optical,electromagnetic, infrared, or semiconductor system, apparatus, device,or propagation medium. More specific examples (a non-exhaustive list) ofthe computer-readable medium may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a transmission media such as those supportingthe Internet or an intranet, or a magnetic storage device. Thecomputer-usable or computer-readable medium may also be paper or anothersuitable medium upon which the program is printed, as the program can beelectronically captured, via, for instance, optical scanning of thepaper or other medium, then compiled, interpreted, or otherwiseprocessed in a suitable manner, if necessary, and then stored in acomputer memory. In the context of this document, a computer-usable orcomputer-readable medium may be any medium that can contain, store,communicate, propagate, or transport the program for use by or inconnection with the instruction execution system, apparatus, or device.The computer-usable medium may include a propagated data signal with thecomputer-usable program code embodied therewith, either in baseband oras part of a carrier wave. The computer usable program code may betransmitted using any appropriate medium, including but not limited tothe Internet, wireline, optical fiber cable, RF, etc.

Computer program code for carrying out operations of the presentdisclosure may be written in an object oriented programming languagesuch as Java, Smalltalk, C++ or the like. However, the computer programcode for carrying out operations of the present disclosure may also bewritten in conventional procedural programming languages, such as the“C” programming language or similar programming languages. The programcode may execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through a local area network/a widearea network/the Internet (e.g., network 14).

The present disclosure is described with reference to flowchartillustrations and/or block diagrams of methods, apparatus (systems) andcomputer program products according to embodiments of the disclosure. Itwill be understood that each block of the flowchart illustrations and/orblock diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, may be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer/special purposecomputer/other programmable data processing apparatus, such that theinstructions, which execute via the processor of the computer or otherprogrammable data processing apparatus, create means for implementingthe functions/acts specified in the flowchart and/or block diagram blockor blocks.

These computer program instructions may also be stored in acomputer-readable memory that may direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

The flowcharts and block diagrams in the figures may illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustrations, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the disclosure.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present disclosure has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the disclosure in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of the disclosure and the practical application, and toenable others of ordinary skill in the art to understand the disclosurefor various embodiments with various modifications as are suited to theparticular use contemplated.

A number of implementations have been described. Having thus describedthe disclosure of the present application in detail and by reference toembodiments thereof, it will be apparent that modifications andvariations are possible without departing from the scope of thedisclosure defined in the appended claims.

What is claimed is:
 1. A computer-implemented method, executed on acomputing device, comprising: monitoring a device to receive datasignals indicative of the device; and processing the data signals over adefined period of time to automatically define one or more definedsignal norms for the data signals.
 2. The computer-implemented method ofclaim 1 wherein the data signals concern one or more details of thedevice and/or uses of the device.
 3. The computer-implemented method ofclaim 1 wherein the device includes one or more of: a medical device, aprocess control device, a networking device, a computing device, amanufacturing device, an agricultural device, an energy/refining device,an aerospace device, a forestry device, and a defense device.
 4. Thecomputer-implemented method of claim 1 wherein processing the datasignals over a defined period of time to automatically define one ormore defined signal norms for the data signals includes: examining arange of the data signals.
 5. The computer-implemented method of claim 1wherein processing the data signals over a defined period of time toautomatically define one or more defined signal norms for the datasignals includes: calculating one or more standard deviations of thedata signals.
 6. The computer-implemented method of claim 1 whereinprocessing the data signals over a defined period of time toautomatically define one or more defined signal norms for the datasignals includes: iteratively redefining the one or more defined signalnorms based upon updated data signals received from the device.
 7. Thecomputer-implemented method of claim 1 wherein processing the datasignals over a defined period of time to automatically define one ormore defined signal norms for the data signals includes: continuouslyredefining the one or more defined signal norms based upon updated datasignals received from the device.
 8. The computer-implemented method ofclaim 1 further comprising: monitoring the device to receive subsequentdata signals indicative of the device; comparing the subsequent datasignals to the defined signal norms to identify outliers; investigatingthe outliers to determine if an issue exists with the device; andadjusting outlier definition criteria to eliminate the outlier if anissue does not exist.
 9. The computer-implemented method of claim 8wherein investigating the outliers to determine if an issue exists withthe device includes one or more of: physically investigating theoutliers; and examining other data signals from the device.
 10. Thecomputer-implemented method of claim 8 wherein adjusting the outlierdefinition criteria includes: defining bespoke outlier definitioncriteria for the device.
 11. The computer-implemented method of claim 1wherein the device includes one or more sub devices.
 12. A computerprogram product residing on a computer readable medium having aplurality of instructions stored thereon which, when executed by aprocessor, cause the processor to perform operations comprising:monitoring a device to receive data signals indicative of the device;and processing the data signals over a defined period of time toautomatically define one or more defined signal norms for the datasignals.
 13. The computer program product of claim 12 wherein the datasignals concern one or more details of the device and/or uses of thedevice.
 14. The computer program product of claim 12 wherein the deviceincludes one or more of: a medical device, a process control device, anetworking device, a computing device, a manufacturing device, anagricultural device, an energy/refining device, an aerospace device, aforestry device, and a defense device.
 15. The computer program productof claim 12 wherein processing the data signals over a defined period oftime to automatically define one or more defined signal norms for thedata signals includes: examining a range of the data signals.
 16. Thecomputer program product of claim 12 wherein processing the data signalsover a defined period of time to automatically define one or moredefined signal norms for the data signals includes: calculating one ormore standard deviations of the data signals.
 17. The computer programproduct of claim 12 wherein processing the data signals over a definedperiod of time to automatically define one or more defined signal normsfor the data signals includes: iteratively redefining the one or moredefined signal norms based upon updated data signals received from thedevice.
 18. The computer program product of claim 12 wherein processingthe data signals over a defined period of time to automatically defineone or more defined signal norms for the data signals includes:continuously redefining the one or more defined signal norms based uponupdated data signals received from the device.
 19. The computer programproduct of claim 12 further comprising: monitoring the device to receivesubsequent data signals indicative of the device; comparing thesubsequent data signals to the defined signal norms to identifyoutliers; investigating the outliers to determine if an issue existswith the device; and adjusting outlier definition criteria to eliminatethe outlier if an issue does not exist.
 20. The computer program productof claim 19 wherein investigating the outliers to determine if an issueexists with the device includes one or more of: physically investigatingthe outliers; and examining other data signals from the device.
 21. Thecomputer program product of claim 19 wherein adjusting the outlierdefinition criteria includes: defining bespoke outlier definitioncriteria for the device.
 22. The computer program product of claim 12wherein the device includes one or more sub devices.
 23. A computingsystem including a processor and memory configured to perform operationscomprising: monitoring a device to receive data signals indicative ofthe device; and processing the data signals over a defined period oftime to automatically define one or more defined signal norms for thedata signals.
 24. The computing system of claim 23 wherein the datasignals concern one or more details of the device and/or uses of thedevice.
 25. The computing system of claim 23 wherein the device includesone or more of: a medical device, a process control device, a networkingdevice, a computing device, a manufacturing device, an agriculturaldevice, an energy/refining device, an aerospace device, a forestrydevice, and a defense device.
 26. The computing system of claim 23wherein processing the data signals over a defined period of time toautomatically define one or more defined signal norms for the datasignals includes: examining a range of the data signals.
 27. Thecomputing system of claim 23 wherein processing the data signals over adefined period of time to automatically define one or more definedsignal norms for the data signals includes: calculating one or morestandard deviations of the data signals.
 28. The computing system ofclaim 23 wherein processing the data signals over a defined period oftime to automatically define one or more defined signal norms for thedata signals includes: iteratively redefining the one or more definedsignal norms based upon updated data signals received from the device.29. The computing system of claim 23 wherein processing the data signalsover a defined period of time to automatically define one or moredefined signal norms for the data signals includes: continuouslyredefining the one or more defined signal norms based upon updated datasignals received from the device.
 30. The computing system of claim 23further comprising: monitoring the device to receive subsequent datasignals indicative of the device; comparing the subsequent data signalsto the defined signal norms to identify outliers; investigating theoutliers to determine if an issue exists with the device; and adjustingoutlier definition criteria to eliminate the outlier if an issue doesnot exist.
 31. The computing system of claim 30 wherein investigatingthe outliers to determine if an issue exists with the device includesone or more of: physically investigating the outliers; and examiningother data signals from the device.
 32. The computing system of claim 30wherein adjusting the outlier definition criteria includes: definingbespoke outlier definition criteria for the device.
 33. The computingsystem of claim 23 wherein the device includes one or more sub devices.